Linear Complexity Research Articles

On the splitting of weak nearly cosymplectic manifolds

Weak almost contact metric manifolds, i.e., the linear complex structure on the contact distribution is replaced by a nonsingular skew-symmetric tensor, defined by the author and R. Wolak (2022), allowed a new look at the theory of contact manifolds. This paper studies the curvature and topology of new structures of this type, called the weak nearly cosymplectic structure and weak nearly Kähler structure. We find conditions under which weak nearly cosymplectic manifolds become Riemannian products and characterize 5-dimensional weak nearly cosymplectic manifolds. Our theorems generalize results by H. Endo (2005) and A. Nicola–G. Dileo–I. Yudin (2018) to the context of weak almost contact geometry.

Ensemble Approach Using k-Partitioned Isolation Forests for the Detection of Stock Market Manipulation

Stock market manipulation, defined as any attempt to artificially influence stock prices, poses significant challenges by causing financial losses and eroding investor trust. The prevalent reliance on supervised learning models for detecting such manipulations, while showing promise, faces notable hurdles due to the dearth of labeled data and the inability to recognize novel manipulation tactics beyond those explicitly labeled. This study ventures into addressing these gaps by proposing a novel detection framework aimed at identifying suspicious hourly manipulation blocks through an unsupervised learning approach, thereby circumventing the limitations of data labeling and enhancing the adaptability to emerging manipulation strategies. Our methodology involves the innovative creation of features reflecting the behavior of stocks across various time windows followed by the segmentation of the dataset into k subsets. This setup facilitates the identification of potential manipulation instances via a voting ensemble composed of k isolation forest models, which have been chosen for their efficiency in pinpointing anomalies and their linear computational complexity—attributes that are critical for analyzing vast datasets. Evaluated against eight real stocks known to have undergone manipulation, our approach demonstrated a remarkable capability to identify up to 89% of manipulated blocks, thus significantly outperforming previous methods that do not utilize a voting ensemble. This finding not only surpasses the detection rates reported in prior studies but also underscores the enhanced robustness and adaptability of our unsupervised model in uncovering varied manipulation schemes. Through this research, we contribute to the field by offering a scalable and efficient unsupervised learning strategy for stock manipulation detection, thereby marking a substantial advancement over traditional supervised methods and paving the way for more resilient financial markets.

Flipping-based iterative surface reconstruction for unoriented points

In this paper, we propose a novel surface reconstruction method for unoriented points by establishing and solving a nonlinear equation system. By treating normals as unknown parameters and imposing the conditions that the implicit field is constant and its gradients parallel to the normals on the input point cloud, we establish a nonlinear equation system involving the oriented normals. To simplify the system, we transform it into a 0-1 integer programming problem solely focusing on orientation by incorporating inconsistent oriented normal information through PCA. We solve the simplified problem using flipping-based iterative algorithms and propose two novel criteria for flipping based on theoretical analysis.Extensive experiments on renowned datasets demonstrate that our flipping-based method with wavelet surface reconstruction achieves state-of-the-art results in orientation and reconstruction. Furthermore, it exhibits linear computational and storage complexity by leveraging the orthogonality and compact support properties of wavelet bases. The source code is available at https://github.com/mayueji/FISR_code.

A Joint Time-Frequency Domain Transformer for multivariate time series forecasting

In order to enhance the performance of Transformer models for long-term multivariate forecasting while minimizing computational demands, this paper introduces the Joint Time-Frequency Domain Transformer (JTFT). JTFT combines time and frequency domain representations to make predictions. The frequency domain representation efficiently extracts multi-scale dependencies while maintaining sparsity by utilizing a small number of learnable frequencies. Simultaneously, the time domain (TD) representation is derived from a fixed number of the most recent data points, strengthening the modeling of local relationships and mitigating the effects of non-stationarity. Importantly, the length of the representation remains independent of the input sequence length, enabling JTFT to achieve linear computational complexity. Furthermore, a low-rank attention layer is proposed to efficiently capture cross-dimensional dependencies, thus preventing performance degradation resulting from the entanglement of temporal and channel-wise modeling. Experimental results on eight real-world datasets demonstrate that JTFT outperforms state-of-the-art baselines in predictive performance.

AlphaFold2 assists in providing novel mechanistic insights into the interactions among the LUBAC subunits.

The linear ubiquitin chain assembly complex (LUBAC) is the only known E3 ligase complex in which the ubiquitin-like (UBL) domains of SHARPIN and HOIL-1L interact with HOIP to determine the structural stability of LUBAC. The interactions between subunits within LUBAC have been a topic of extensive research. However, the impact of the LTM motif on the interaction between the UBL domains of SHARPIN and HOIL-1L with HOIP remains unclear. Here, we discover that the absence of the LTM motif in the AlphaFold2-predicted LUBAC structure alters the HOIP-UBA structure. We employ GeoPPI to calculate the changes in binding free energy (ΔG) caused by single-point mutations between subunits, simulating their protein-protein interactions. The results reveal that the presence of the LTM motif decreases the interaction between the UBL domains of SHARPIN and HOIL-1L with HOIP, leading to a decrease in the structural stability of LUBAC. Furthermore, using the AlphaFold2-predicted results, we find that HOIP (629‒695) and HOIP-UBA bind to both sides of HOIL-1L-UBL, respectively. The experiments of Gromacs molecular dynamics simulations, SPR and ITC demonstrate that the elongated domain formed by HOIP (629‒695) and HOIP-UBA, hereafter referred to as the HOIP (466‒695) structure, interacts with HOIL-1L-UBL to form a structurally stable complex. These findings illustrate the collaborative interaction between HOIP-UBA and HOIP (629‒695) with HOIL-1L-UBL, which influences the structural stability of LUBAC.

Circuit complexity of quantum access models for encoding classical data

How to efficiently encode classical data is a fundamental task in quantum computing. While many existing works treat classical data encoding as a black box in oracle-based quantum algorithms, their explicit constructions are crucial for the efficiency of practical algorithm implementations. Here, we unveil the mystery of the classical data encoding black box and study the Clifford + T complexity in constructing several typical quantum access models. For general matrices (even including sparse ones), we prove that sparse-access input models and block-encoding both require nearly linear circuit complexities relative to the matrix dimension. We also give construction protocols achieving near-optimal gate complexities. On the other hand, the construction becomes efficient with respect to the data qubit when the matrix is a linear combination of polynomial terms of efficiently implementable unitaries. As a typical example, we propose improved block-encoding when these unitaries are Pauli strings. Our protocols are built upon improved quantum state preparation and a select oracle for Pauli strings, which hold independent values. Our access model constructions provide considerable flexibility, allowing for tunable ancillary qubit numbers and offering corresponding space-time trade-offs.

A de novo dominant-negative variant is associated with OTULIN-related autoinflammatory syndrome.

OTULIN-related autoinflammatory syndrome (ORAS), a severe autoinflammatory disease, is caused by biallelic pathogenic variants of OTULIN, a linear ubiquitin-specific deubiquitinating enzyme. Loss of OTULIN attenuates linear ubiquitination by inhibiting the linear ubiquitin chain assembly complex (LUBAC). Here, we report a patient who harbors two rare heterozygous variants of OTULIN (p.P152L and p.R306Q). We demonstrated accumulation of linear ubiquitin chains upon TNF stimulation and augmented TNF-induced cell death in mesenchymal stem cells differentiated from patient-derived iPS cells, which confirms that the patient has ORAS. However, although the de novo p.R306Q variant exhibits attenuated deubiquitination activity without reducing the amount of OTULIN, the deubiquitination activity of the p.P152L variant inherited from the mother was equivalent to that of the wild-type. Patient-derived MSCs in which the p.P152L variant was replaced with wild-type also exhibited augmented TNF-induced cell death and accumulation of linear chains. The finding that ORAS can be caused by a dominant-negative p.R306Q variant of OTULIN furthers our understanding of disease pathogenesis.

Adaptive fast multiplication of ℋ²-matrices

Hierarchical matrices approximate a given matrix by a decomposition into low-rank submatrices that can be handled efficiently in factorized form. H 2 \mathcal {H}^2 -matrices refine this representation following the ideas of fast multipole methods in order to achieve linear, i.e., optimal complexity for a variety of important algorithms. The matrix multiplication, a key component of many more advanced numerical algorithms, has been a challenge: the only linear-time algorithms known so far either require the very special structure of HSS-matrices or need to know a suitable basis for all submatrices in advance. In this article, a new and fairly general algorithm for multiplying H 2 \mathcal {H}^2 -matrices in linear complexity with adaptively constructed bases is presented. The algorithm consists of two phases: first an intermediate representation with a generalized block structure is constructed, then this representation is re-compressed in order to match the structure prescribed by the application. The complexity and accuracy are analyzed and numerical experiments indicate that the new algorithm can indeed be significantly faster than previous attempts.

Mercury‐Group 13 Metal Covalent Bonds: A Systematic Comparison of Aluminyl, Gallyl and Indyl Metallo‐ligands

AbstractBimetallic compounds containing direct metal‐group 13 element bonds have been shown to display unprecedented patterns of cooperative reactivity towards small molecules, which can be influenced by the identity of the group 13 element. In this context, we present here a systematic appraisal of group 13 metallo‐ligands of the type [(NON)E]− (NON=4,5‐bis(2,6‐diisopropylanilido)‐2,7‐di‐tert‐butyl‐9,9‐dimethylxanthene) for E=Al, Ga and In, through a comparison of structural and spectroscopic parameters associated with the trans L or X ligands in linear d10 complexes of the types LM{E(NON)} and XM′{E(NON)}. These studies are facilitated by convenient syntheses (from the In(I) precursor, InCp) of the potassium indyl species [{K(NON)In}⋅KCp]n (1) and [(18‐crown‐6)2K2Cp] [(NON)In] (1′), and lead to the first structural characterisation of Ag−In and Hg−E (E=Al, In) covalent bonds. The resulting structural, spectroscopic and quantum chemical probes of Ag/Hg complexes are consistent with markedly stronger σ‐donor capabilities of the aluminyl ligand, [(NON)Al]−, over its gallium and indium counterparts.

Mercury-Group 13 Metal Covalent Bonds: A Systematic Comparison of Aluminyl, Gallyl and Indyl Metallo-ligands.

Bimetallic compounds containing direct metal-group 13 element bonds have been shown to display unprecedented patterns of cooperative reactivity towards small molecules, which can be influenced by the identity of the group 13 element. In this context, we present here a systematic appraisal of group 13 metallo-ligands of the type [(NON)E]- (NON=4,5-bis(2,6-diisopropylanilido)-2,7-di-tert-butyl-9,9-dimethylxanthene) for E=Al, Ga and In, through a comparison of structural and spectroscopic parameters associated with the trans L or X ligands in linear d10 complexes of the types LM{E(NON)} and XM'{E(NON)}. These studies are facilitated by convenient syntheses (from the In(I) precursor, InCp) of the potassium indyl species [{K(NON)In}⋅KCp]n (1) and [(18-crown-6)2K2Cp] [(NON)In] (1'), and lead to the first structural characterisation of Ag-In and Hg-E (E=Al, In) covalent bonds. The resulting structural, spectroscopic and quantum chemical probes of Ag/Hg complexes are consistent with markedly stronger σ-donor capabilities of the aluminyl ligand, [(NON)Al]-, over its gallium and indium counterparts.

Dominant negative OTULIN-related autoinflammatory syndrome.

OTU deubiquitinase with linear linkage specificity (OTULIN) regulates inflammation and cell death by deubiquitinating linear ubiquitin chains generated by the linear ubiquitin chain assembly complex (LUBAC). Biallelic loss-of-function mutations causes OTULIN-related autoinflammatory syndrome (ORAS), while OTULIN haploinsuffiency has not been associated with spontaneous inflammation. However, herein, we identify two patients with the heterozygous mutation p.Cys129Ser in OTULIN. Consistent with ORAS, we observed accumulation of linear ubiquitin chains, increased sensitivity to TNF-induced death, and dysregulation of inflammatory signaling in patient cells. While the C129S mutation did not affect OTULIN protein stability or binding capacity to LUBAC and linear ubiquitin chains, it did ablate OTULIN deubiquitinase activity. Loss of activity facilitated the accumulation of autoubiquitin chains on LUBAC. Altered ubiquitination of LUBAC inhibits its recruitment to the TNF receptor signaling complex, promoting TNF-induced cell death and disease pathology. By reporting the first dominant negative mutation driving ORAS, this study expands our clinical understanding of OTULIN-associated pathology.

MEAformer: An all-MLP transformer with temporal external attention for long-term time series forecasting

Transformer-based models have significantly improved performance in Long-term Time Series Forecasting (LTSF). These models employ various self-attention mechanisms to discover long-term dependencies. However, the computational efficiency is hampered by the inherent permutation invariance of self-attention, and they primarily focus on relationships within the sequence while neglecting potential relationships between different sample sequences. This limits the ability and flexibility of self-attention in LTSF. In addition, the Transformer's decoder outputs sequences in an autoregressive manner, leading to slow inference speed and error accumulation effects, especially for LTSF. Regarding the issues with Transformer-based models for LTSF, we propose a model better suited for LTSF, named MEAformer. MEAformer adopts a fully connected Multi-Layer Perceptron (MLP) architecture consisting of two types of layers: encoder layers and MLP layers. Unlike most encoder layers in Transformer-based models, the MEAformer replaces self-attention with temporal external attention. Temporal external attention explores potential relationships between different sample sequences in the training dataset. Compared to the quadratic complexity of self-attention mechanisms, temporal external attention has efficient linear complexity. Encoder layers can be stacked multiple times to capture time-dependent relationships at different scales. Furthermore, the MEAformer replaces the intricate decoder layers of the original model with more straightforward MLP layers. This modification aims to enhance inference speed and facilitate single-pass sequence generation, effectively mitigating the problem of error accumulation effects. Regarding long-term forecasting, MEAformer achieves state-of-the-art performance on six benchmark datasets, covering five real-world domains: energy, transportation, economy, weather, and disease. Code is available at: https://github.com/huangsiyuan924/MEAformer.

Biallelic human SHARPIN loss of function induces autoinflammation and immunodeficiency.

The linear ubiquitin assembly complex (LUBAC) consists of HOIP, HOIL-1 and SHARPIN and is essential for proper immune responses. Individuals with HOIP and HOIL-1 deficiencies present with severe immunodeficiency, autoinflammation and glycogen storage disease. In mice, the loss of Sharpin leads to severe dermatitis due to excessive keratinocyte cell death. Here, we report two individuals with SHARPIN deficiency who manifest autoinflammatory symptoms but unexpectedly no dermatological problems. Fibroblasts and B cells from these individuals showed attenuated canonical NF-κB responses and a propensity for cell death mediated by TNF superfamily members. Both SHARPIN-deficient and HOIP-deficient individuals showed a substantial reduction of secondary lymphoid germinal center B cell development. Treatment of one SHARPIN-deficient individual with anti-TNF therapies led to complete clinical and transcriptomic resolution of autoinflammation. These findings underscore the critical function of the LUBAC as a gatekeeper for cell death-mediated immune dysregulation in humans.

Accurately clustering biological sequences in linear time by relatedness sorting

Clustering biological sequences into similar groups is an increasingly important task as the number of available sequences continues to grow exponentially. Search-based approaches to clustering scale super-linearly with the number of input sequences, making it impractical to cluster very large sets of sequences. Approaches to clustering sequences in linear time currently lack the accuracy of super-linear approaches. Here, I set out to develop and characterize a strategy for clustering with linear time complexity that retains the accuracy of less scalable approaches. The resulting algorithm, named Clusterize, sorts sequences by relatedness to linearize the clustering problem. Clusterize produces clusters with accuracy rivaling popular programs (CD-HIT, MMseqs2, and UCLUST) but exhibits linear asymptotic scalability. Clusterize generates higher accuracy and oftentimes much larger clusters than Linclust, a fast linear time clustering algorithm. I demonstrate the utility of Clusterize by accurately solving different clustering problems involving millions of nucleotide or protein sequences.

Systematic HOIP interactome profiling reveals critical roles of linear ubiquitination in tissue homeostasis

Linear ubiquitination catalyzed by HOIL-1-interacting protein (HOIP), the key component of the linear ubiquitination assembly complex, plays fundamental roles in tissue homeostasis by executing domain-specific regulatory functions. However, a proteome-wide analysis of the domain-specific interactome of HOIP across tissues is lacking. Here, we present a comprehensive mass spectrometry-based interactome profiling of four HOIP domains in nine mouse tissues. The interaction dataset provides a high-quality HOIP interactome resource with an average of approximately 90 interactors for each bait per tissue. HOIP tissue interactome presents a systematic understanding of linear ubiquitination functions in each tissue and also shows associations of tissue functions to genetic diseases. HOIP domain interactome characterizes a set of previously undefined linear ubiquitinated substrates and elucidates the cross-talk among HOIP domains in physiological and pathological processes. Moreover, we show that linear ubiquitination of Integrin-linked protein kinase (ILK) decreases focal adhesion formation and promotes the detachment of Shigella flexneri-infected cells. Meanwhile, Hoip deficiency decreases the linear ubiquitination of Smad ubiquitination regulatory factor 1 (SMURF1) and enhances its E3 activity, finally causing a reduced bone mass phenotype in mice. Overall, our work expands the knowledge of HOIP-interacting proteins and provides a platform for further discovery of linear ubiquitination functions in tissue homeostasis.

Dual-Dependency Attention Transformer for Fine-Grained Visual Classification.

Visual transformers (ViTs) are widely used in various visual tasks, such as fine-grained visual classification (FGVC). However, the self-attention mechanism, which is the core module of visual transformers, leads to quadratic computational and memory complexity. The sparse-attention and local-attention approaches currently used by most researchers are not suitable for FGVC tasks. These tasks require dense feature extraction and global dependency modeling. To address this challenge, we propose a dual-dependency attention transformer model. It decouples global token interactions into two paths. The first is a position-dependency attention pathway based on the intersection of two types of grouped attention. The second is a semantic dependency attention pathway based on dynamic central aggregation. This approach enhances the high-quality semantic modeling of discriminative cues while reducing the computational cost to linear computational complexity. In addition, we develop discriminative enhancement strategies. These strategies increase the sensitivity of high-confidence discriminative cue tracking with a knowledge-based representation approach. Experiments on three datasets, NABIRDS, CUB, and DOGS, show that the method is suitable for fine-grained image classification. It finds a balance between computational cost and performance.

Hierarchical Patch Aggregation Transformer for Motion Deblurring

The encoder-decoder framework based on Transformer components has become a paradigm in the field of image deblurring architecture design. In this paper, we critically revisit this approach and find that many current architectures severely focus on limited local regions during the feature extraction stage. These designs compromise the feature richness and diversity of the encoder-decoder framework, leading to bottlenecks in performance improvement. To address these deficiencies, a novel Hierarchical Patch Aggregation Transformer architecture (HPAT) is proposed. In the initial feature extraction stage, HPAT combines Axis-Selective Transformer Blocks with linear complexity and is supplemented by an adaptive hierarchical attention fusion mechanism. These mechanisms enable the model to effectively capture the spatial relationships between features and integrate features from different hierarchical levels. Then, we redesign the feedforward network of the Transformer block in the encoder-decoder structure and propose the Fused Feedforward Network. This effective aggregation enhances the ability to capture and retain local detailed features. We evaluate HPAT through extensive experiments and compare its performance with baseline methods on public datasets. Experimental results show that the proposed HPAT model achieves state-of-the-art performance in image deblurring tasks.

NEW THRESHOLD PRIVATE SET INTERSECTION PROTOCOLS

With the rising amount of digital technologies that we use on a daily basis, it is more important than ever to handle and process private data securely. Research and academic communities are becoming increasingly interested in multi-party computation, with a focus on the field of Private Set Intersection (PSI). In this regard, this work introduces a novel technique that successfully converts the Cid-Davidson Private Set Intersection protocol into a Threshold Private Set Intersection. It achieves this conversion by introducing two new protocols, TPSI-1 and TPSI-2, and utilizing two previously developed methodologies while the Reed-Solomon codes and the Shamir-secret sharing scheme are the foundations of TPSI-1, whereas Secure Comparison Protocols serve as the foundation for TPSI-2. Specifically, our suggested protocols perform better asymptotically than previous threshold PSI protocols because they have a fixed number of rounds and linear communication and computation complexity that increase with data set size. This study adds to the continuous effort to strengthen the security and effectiveness of private data calculations, highlighting how safe data processing is changing in an era where digital technologies are ingrained in every aspect of our lives.

IN-MEMORY INTELLIGENT COMPUTING

Context. Processed big data has social significance for the development of society and industry. Intelligent processing of big data is a condition for creating a collective mind of a social group, company, state and the planet as a whole. At the same time, the economy of big data (Data Economy) takes first place in the evaluation of processing mechanisms, since two parameters are very important: speed of data processing and energy consumption. Therefore, mechanisms focused on parallel processing of large data within the data storage center will always be in demand on the IT market.
 Objective. The goal of the investigation is to increase the economy of big data (Data Economy) thanks to the analysis of data as truth table addresses for the identification of patterns of production functionalities based on the similarity-difference metric.
 Method. Intelligent computing architectures are proposed for managing cyber-social processes based on monitoring and analysis of big data. It is proposed to process big data as truth table addresses to solve the problems of identification, clustering, and classification of patterns of social and production processes. A family of automata is offered for the analysis of big data, such as addresses. The truth table is considered as a reasonable form of explicit data structures that have a useful constant – a standard address routing order. The goal of processing big data is to make it structured using a truth table for further identification before making actuator decisions. The truth table is considered as a mechanism for parallel structuring and packing of large data in its column to determine their similarity-difference and to equate data at the same addresses. Representation of data as addresses is associated with unitary encoding of patterns by binary vectors on the found universe of primitive data. The mechanism is focused on processorless data processing based on read-write transactions using in-memory computing technology with significant time and energy savings. The metric of truth table big data processing is parallelism, technological simplicity, and linear computational complexity. The price for such advantages is the exponential memory costs of storing explicit structured data.
 Results. Parallel algorithms of in-memory computing are proposed for economic mechanisms of transformation of large unstructured data, such as addresses, into useful structured data. An in-memory computing architecture with global feedback and an algorithm for matrix parallel processing of large data such as addresses are proposed. It includes a framework for matrix analysis of big data to determine the similarity between vectors that are input to the matrix sequencer. Vector data analysis is transformed into matrix computing for big data processing. The speed of the parallel algorithm for the analysis of big data on the MDV matrix of deductive vectors is linearly dependent on the number of bits of the input vectors or the power of the universe of primitives. A method of identifying patterns using key words has been developed. It is characterized by the use of unitary coded data components for the synthesis of the truth table of the business process. This allows you to use read-write transactions for parallel processing of large data such as addresses.
 Conclusions. The scientific novelty consists in the development of the following innovative solutions: 1) a new vector-matrix technology for parallel processing of large data, such as addresses, is proposed, characterized by the use of read-write transactions on matrix memory without the use of processor logic; 2) an in-memory computing architecture with global feedback and an algorithm for matrix parallel processing of large data such as addresses are proposed; 3) a method of identifying patterns using keywords is proposed, which is characterized by the use of unitary coded data components for the synthesis of the truth table of the business process, which makes it possible to use the read-write transaction for parallel processing of large data such as addresses. The practical significance of the study is that any task of artificial intelligence (similarity-difference, classification-clustering and recognition, pattern identification) can be solved technologically simply and efficiently with the help of a truth table (or its derivatives) and unitarily coded big data . Research prospects are related to the implementation of this digital modeling technology devices on the EDA market. KEYWORDS: Intelligent

Auxiliary two-filter particle smoothing for one generalized hidden Markov model

This paper develops two-filter particle smoothing (TFPS) algorithms for the nonlinear fixed-interval smoothing problem of one generalized hidden Markov model (GHMM), where the current observation depends not only on the current state, but also on one-step previous state. Firstly, by Bayesian approach, the two-filter smoothing (TFS) formula for GHMM is established to calculate smoothing densities. In this TFS formula, the backward information prediction density is generally not a density of the state. This results in a difficulty that the normal sequential Monte Carlo (SMC) sampling technique cannot be directly applied to design corresponding TFPS algorithms based on the TFS formula. To solve this difficulty, a generalized TFS formula for GHMM is then proposed by introducing a sequence of artificial densities. By combining this generalized TFS formula, SMC, and the auxiliary variable sampling technique, a basic auxiliary TFPS (ATFPS) algorithm with quadratic computational complexity is proposed, and a simplified ATFPS algorithm with linear computational complexity is further developed. Finally, the effectiveness and superiority of the two proposed ATFPS algorithms for GHMM are verified via simulation examples and real experimental data.