Time Complexity Research Articles

On the circuit model of two quantum adiabatic search algorithms

The early work of Wei et al. has shown that it is possible to do the computation in constant time for quantum search in quantum local adiabatic evolution framework if the system Hamiltonian has an extra enlarged factor proportional to the square-root of the size of the problem. Also in our prior work, we have shown that it is possible to apply a linear interpolating path in the system Hamiltonian to achieve an O(1) time complexity for a quantum global adiabatic computation. In this paper, we discuss the issues when implementing these two quantum algorithms on the quantum circuit model, and find that to our surprise, the time slices produced does not equal to the time complexity of the algorithm in each case. This is in contrast to the previous related results, because there these two quantities always coincide. When taking into account of the whole energy-level increase of the system compared with that of the usual quantum adiabatic evolutions and the corresponding time complexity simultaneously, we find that the time slices needed always consist of the two quantities multiplying together. This result is new, and may be hopeful to find application in designing quantum adiabatic algorithm for problems beyond quantum search.

Krisp: A Python package to aid in the design of CRISPR and amplification-based diagnostic assays from whole genome sequencing data.

Recent pandemics like COVID-19 highlighted the importance of rapidly developing diagnostics to detect evolving pathogens. CRISPR-Cas technology has recently been used to develop diagnostic assays for sequence-specific recognition of DNA or RNA. These assays have similar sensitivity to the gold standard qPCR but can be deployed as easy to use and inexpensive test strips. However, the discovery of diagnostic regions of a genome flanked by conserved regions where primers can be designed requires extensive bioinformatic analyses of genome sequences. We developed the Python package krisp to aid in the discovery of primers and diagnostic sequences that differentiate groups of samples from each other, using either unaligned genome sequences or a variant call format (VCF) file as input. Krisp has been optimized to handle large datasets by using efficient algorithms that run in near linear time, use minimal RAM, and leverage parallel processing when available. The validity of krisp results has been demonstrated in the laboratory with the successful design of a CRISPR diagnostic assay to distinguish the sudden oak death pathogen Phytophthora ramorum from closely related Phytophthora species. Krisp is released open source under a permissive license with all the documentation needed to quickly design CRISPR-Cas diagnostic assays.

Scaling and Universality at Ramped Quench Dynamical Quantum Phase Transitions.

The nonequilibrium dynamics of a periodically driven extended XY model, in the presence of
linear time dependent magnetic filed, is investigated using the notion of dynamical quantum phase
transitions (DQPTs). Along the similar lines to the equilibrium phase transition, the main purpose
of this work is to search the fundamental concepts such as scaling and universality at the ramped
quench DQPTs. We have shown that the critical points of the model, where the gap closing occurs,
can be moved by tuning the driven frequency and consequently the presence/absence of DQPTs
can be flexibly controlled by adjusting the driven frequency. We have uncovered that, for a ramp
across the single quantum critical point, the critical mode at which DQPTs occur is classified into
three regions: the Kibble-Zurek (KZ) region, where the critical mode scales linearly with the square
root of the sweep velocity, pre-saturated (PS) region, and the saturated (S) region where the critical
mode makes a plateau versus the sweep velocity. While for a ramp that crosses two critical points,
the critical modes disclose just KZ and PS regions. On the basis of numerical simulations, we find
that the dynamical free energy scales linerly with time, as approaches to DQPT time, with the
exponent ν = 1 ± 0.01 for all sweep velocities and driven frequencies.

Robust Incremental Broad Learning System for Data Streams of Uncertain Scale.

Due to its marvelous performance and remarkable scalability, a broad learning system (BLS) has aroused a wide range of attention. However, its incremental learning suffers from low accuracy and long training time, especially when dealing with unstable data streams, making it difficult to apply in real-world scenarios. To overcome these issues and enrich its relevant research, a robust incremental BLS (RI-BLS) is proposed. In this method, the proposed weight update strategy introduces two memory matrices to store the learned information, thus the computational procedure of ridge regression is decomposed, resulting in precomputed ridge regression. During incremental learning, RI-BLS updates two memory matrices and renews weights via precomputed ridge regression efficiently. In addition, this update strategy is theoretically analyzed in error, time complexity, and space complexity compared with existing incremental BLSs. Different from Greville's method used in the original incremental BLS, its results are closer to the solution of one-shot calculation. Compared with the existing incremental BLSs, the proposed method exhibits more stable time complexity and superior space complexity. The experiments prove that RI-BLS outperforms other incremental BLSs when handling both stable and unstable data streams. Furthermore, experiments demonstrate that the proposed weight update strategy applies to other random neural networks as well.

The mundane governance of education through time: the case of national testing in Norway

ABSTRACT This article explores national testing in Norway by analyzing technical and bureaucratic documents steering the process of data production. By examining documents through sociological perspectives on time, as well as attuning to the form and genre of the documents, we identify bureaucratic, sociotechnical, and genre-specific ways in which data production is structured to ensure a smooth process of testing. We find that national tests govern (through) time and by ordering time: their time rules produce simultaneity and synchronicity that in turn gently nudge schools and municipalities into alignment. Thus, time rules and routines enable forms of mundane governance that mirror how contemporary education policy happens through short but more prescriptive (and increasingly digital) policy texts. This is how national authorities may make themselves less detectable, but ever more present and productive of national unity. While recent conversations on time and temporality focus on the emergence of complex, non-linear (e.g. networked) time enabled by the digital transformation of society and education, our article attests to the persistence and significance of linear temporalities in and for education governance, and shows how quantification, assessment, bureaucracy and the affordances of digital technologies together play pivotal roles in upholding a linear time order.

Coordinate-Free and Low-Order Scaling Machine Learning Model for Atomic Partial Charge Prediction for Any Size of Molecules.

The atomic partial charge is of great importance in many fields, such as chemistry and drug-target recognition. However, conventional quantum-based computing of atomic charges is relatively slow, limiting further applications of atomic charge analysis. With the help of machine learning methods, various kinds of models appear to speed up atomic charge calculations. However, there are still some concerning problems. Some models based on geometric coordinates require high-accuracy geometry optimization as a preprocess, while other models have a limitation on the size of input molecules that narrow the applications of the model. Here, we propose a machine learning atomic charge model based on a message-passing featurizer. This preprocessing featurizer can quickly extract atomic environment information from a molecule according to the connectivity inside the molecule. The resulting descriptor can be used with a neural network to quickly predict the atomic partial charge. The model is able to automatically adapt to any size of molecule while remaining efficient and achieves a root-mean-square error in the Hirshfeld charge prediction of 0.018e, with an overall time complexity of O(n2). Thus, this model could enlarge the range of applications of atomic partial charge to more fields and cases.

Rational Kriging

This article proposes a new kriging that has a rational form. It is shown that the generalized least squares estimator of the mean from rational kriging is much more well behaved than that of ordinary kriging. Parameter estimation and uncertainty quantification for rational kriging are proposed using a Gaussian process framework. A generalized version of rational kriging is also proposed, which includes ordinary and rational kriging as special cases. Extensive simulations carried out over a wide class of functions show that the generalized rational kriging performs on par or better than both ordinary and rational kriging in terms of prediction and uncertainty quantification. The only extra step needed for generalized rational kriging over ordinary kriging is the computation of Perron eigenvector of an augmented correlation matrix which can be computed in near linear time and therefore, its overall computational complexity is no more than that of ordinary kriging. The potential applications of the new kriging methods in the emulation of computationally expensive models and model calibration problems are illustrated with real and simulated examples.

A Family of Centrality Measures for Graph Data Based on Subgraphs

We present the theoretical foundations and first experimental study of a new approach in centrality measures for graph data. The main principle is straightforward: the more relevant subgraphs around a vertex, the more central it is in the network. We formalize the notion of “relevant subgraphs” by choosing a family of subgraphs that, given a graph G and a vertex v , assigns a subset of connected subgraphs of G that contains v . Any of such families defines a measure of centrality by counting the number of subgraphs assigned to the vertex, i.e., a vertex will be more important for the network if it belongs to more subgraphs in the family. We show several examples of this approach. In particular, we propose the All-Subgraphs (All-Trees) centrality, a centrality measure that considers every subgraph (tree). We study fundamental properties over families of subgraphs that guarantee desirable properties over the centrality measure. Interestingly, All-Subgraphs and All-Trees satisfy all these properties, showing their robustness as centrality notions. To conclude the theoretical analysis, we study the computational complexity of counting certain families of subgraphs and show a linear time algorithm to compute the All-Subgraphs and All-Trees centrality for graphs with bounded treewidth. Finally, we implemented these algorithms and computed these measures over more than one hundred real-world networks. With this data, we present an empirical comparison between well-known centrality measures and those proposed in this work.

Enforcing Temporal Consistency in Migration History Inference.

In addition to undergoing evolution, members of biological populations may also migrate between locations. Examples include the spread of tumor cells from the primary tumor to distant metastases or the spread of pathogens from one host to another. One may represent migration histories by assigning a location label to each vertex of a given phylogenetic tree such that an edge connecting vertices with distinct locations represents a migration. Some biological populations undergo comigration, a phenomenon where multiple taxa from distinct lineages simultaneously comigrate from one location to another. In this work, we show that a previous problem statement for inferring migration histories that are parsimonious in terms of migrations and comigrations may lead to temporally inconsistent solutions. To remedy this deficiency, we introduce precise definitions of temporal consistency of comigrations in a phylogenetic tree, leading to three successive problems. First, we formulate the temporally consistent comigration problem to check if a set of comigrations is temporally consistent and provide a linear time algorithm for solving this problem. Second, we formulate the parsimonious consistent comigrations (PCC) problem, which aims to find comigrations given a location labeling of a phylogenetic tree. We show that PCC is NP-hard. Third, we formulate the parsimonious consistent comigration history (PCCH) problem, which infers the migration history given a phylogenetic tree and locations of its extant vertices only. We show that PCCH is NP-hard as well. On the positive side, we propose integer linear programming models to solve the PCC and PCCH problems. We demonstrate our algorithms on simulated and real data.

Association of plasma biomarkers of Alzheimer's disease and related disorders with cognition and cognitive decline: The MYHAT population-based study.

Plasma biomarkers of Alzheimer's disease and related dementias predict global cognitive performance and decline over time; it remains unclear how they associate with changes in different dementia syndromes affecting distinct cognitive domains. In a prospective study with repeated assessments of a randomly selected population-based cohort (n=787, median age 73), we evaluated performance and decline in different cognitive domains over up to 8 years in relation to plasma concentrations of amyloid beta 42/40 (Aβ42/40) ratio, phosphorylated tau181 (p-tau181), neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP). Cross-sectionally, memory showed the strongest associations with p-tau181, and attention, executive, and visuospatial functions with NfL.Longitudinally, memory decline was distinguishable with all biomarker profiles dichotomized according to data-driven cutoffs, most efficiently with Aβ42/40. GFAP and Aβ42/40 were the best discriminators of decline patterns in language and visuospatial functions, respectively. These relatively non-invasive tests may be beneficial for clinical screening after replication in other populations and validation through neuroimaging or cerebrospinal fluid analysis. We performed a prospective study with up to 8 years of repeated domain-specific cognitive assessments and baseline plasma Alzheimer's disease and related dementias biomarker measurements in a randomly selected population-based cohort. We considered distinct growth curves of trajectories of different cognitive domains and survival bias induced by missing data by adding quadratic time and applying joint modeling technique. Cross-sectionally, memory showed the strongest associations with plasma phosphorylated tau181, while attention, executive, and visuospatial functions were most strongly associated with neurofilament light chain. Longitudinally, memory and visuospatial declines were most efficiently distinguished by dichotomized amyloid beta 42/40 profile among all plasma biomarkers, while language was by dichotomized glial fibrillary acidic protein. These relatively non-invasive tests may be beneficial for clinical screening; however, they will need replication in other populations and validation through neuroimaging and/or cerebrospinal fluid assessments.

Optimized deep neural network to estimate orientation angles for solar photovoltaics intelligent systems

Using a single hidden layer neural network in estimating orientation angles for solar photovoltaics lacks the complexity required to model nonlinear relationships between input variables and the optimal orientation angles for solar photovoltaics. It struggles to generalize well to new and unseen data. More sophisticated neural network architectures such as deep learning with multi-hidden perceptron (MLP) can solve these issues by changing the architecture by deepening the network. Deepening the network will increase complexity, energy consumption, and time complexity. The study uses a novel approach to outperform traditional MLP models with two, three, four, and five hidden layers. An innovative approach was proposed by enhancing a single hidden layer MLP with a quadratic polynomial function, utilizing two robust methodologies, Least Absolute Residuals (LAR) and Bisquare methods. The results demonstrate that these approaches yield significant improvements in Root Mean Square Error (RMSE) and coefficient of determination (R squared). LAR-based MLP showed superiority over both bisquare-based and conventional MLPs methods in R2 and RMSE, ranging from 1.13 to 1.18 and 2.53 to 3.06, respectively. The study outperformed conventional MLP architectures with five hidden layers regarding accuracy and efficiency. The proposed model offers a more effective and less complex solution for data prediction tasks.

Distant time: The future of urbanisation from ‘there’ and ‘then’

Recent geographical scholarship has mainly focussed on the disjunctures between linear and cyclical time in urban development. This paper proposes a notion of distant time as a metaphor of temporal power that keeps marginal citizens at a governable distance from the state. Taking the case of Shimla, an erstwhile Summer Capital of colonial India and a popular tourist town in the Himalayas, it argues that distant time emerges from the temporal reordering of ‘native’ settlement on a fragile ecological landscape ravaged by the colonial state, that is then repeated in postcolonial imaginaries of smart urban futures. Reading ‘along the grain’ of colonial archives of incremental housebuilding by the ‘natives’, as well as interviews with current working class residents of Shimla living under threat of demolition from proposed smart city projects, this paper suggests that distant time is also a space for marginal citizens to claim temporal justice. Even as the state engages in temporal distancing through post/colonial planning, marginal citizens use waiting, confusing, and circumventing as tools of temporal arbitrage. They highlight that aspirations for smart urban futures are not just produced in the ‘here and now’ of the present, but also from the ‘there and then’ of different pasts and futures.

Shaping the time to be a good teacher: a case study on teacher excellence and time ownership in a British transnational university

ABSTRACT This study is about a lecturer protecting herself and her teaching from the university’s increasing demands on her personal and timeless time. The British university is shaped by a fundamental arrhythmia: the co-existence of digital time, that academics are encouraged to embrace working from anywhere at any time; and analogical time, the linear time of classes, deadlines, and required administrative tasks. Pressured by these competing demands on their time and responsibilities, research and surveys show that academics feel alienated as university time arrhythmia ‘devours’ both their thinking and personal time and can compromise their wellbeing. This case study aims at uncovering the connection between the lecturer’s practices of teaching excellence, her administrative work and university time. Qualitative content analysis of semi-structured interviews illustrates how university time and teaching excellence are related, and university arrhythmia can actually be used to protect good teaching. This lecturer skilfully managed university time-devouring arrhythmia: when the university used an analogical logic of time, she used digital time, and vice versa. By doing this, she protected her personal and timeless time as well as her own teaching from mounting demands of teacher excellence, measured by the university’s simultaneous and conflicting digital and analogical logics of time.

Stability Analysis via Impedance Modelling of a Real-World Wind Generation System with AC Collector and LCC-Based HVDC Transmission Grid

This paper studies the stability of a real-world wind farm, Bison Wind Generation System (BWGS) in the state of North Dakota in the United States. BWGS uses an AC collector grid rated at 34.5 kV and a symmetrical bipolar high-voltage DC (HVDC) transmission grid rated at ±250 kV. The HVDC line transfers a total power of 0.5 GW, while both the HVDC rectifier and inverter substations use line-commuted converters (LCCs). The LCC-based rectifier adopts constant DC current control to regulate HVDC current, while the inverter operates in constant extinction angle control mode to maintain a fixed HVDC voltage. This paper proposes a frequency scan-based approach to obtain the d–q impedance model of (i) BWGS AC collector grids with Type 4 wind turbines that use permanent magnet synchronous generators (PMSGs) and two fully rated converters, and (ii) an LCC-HVDC system. The impedance frequency response of the BWGS is acquired by exciting the AC collector grid and LCC-HVDC with multi-sine voltage perturbations during its steady-state operation. The resulting voltage and current signals are subjected to a fast Fourier transform (FFT) to extract frequency components. By analyzing the impedance frequency response measurement of BWGS, a linear time–invariant (LTI) representation of its dynamics is obtained using the vector fitting (VF) technique. Finally, a Bode plot is applied, considering the impedance of the BWGS and grid to perform stability analyses. This study examines the influence of the short circuit ratio (SCR) of the grid and the phase lock loop (PLL) frequency bandwidth on the stability of the overall system. The findings provide valuable insights for the design and verification of an AC collector and LCC-based HVDC transmission systems. The findings suggest that the extraction of the impedance model of a real-world wind farm, achieved through frequency scanning and subsequent representation as an LTI system using VF, is regarded as a robust, suitable, and accurate methodology for investigating the dynamics, unstable operating conditions, and control interaction of the wind farm and LCC-HVDC system with the AC grid.

Unveiling the Neutral Difference and Its Automated Search

Given a differential characteristic and an existing plaintext pair that satisfies it (referred to as a right pair), generating additional right pairs at a reduced cost is an appealing prospect. The neutral bit technique, referred to as neutral differences throughout this paper, provides a solution to this challenge. Traditionally, the search for neutral differences has heavily depended on experimental testing, leading to limitations in the search range. In this work, we propose the neutral difference table and establish a link between boomerang cryptanalysis and neutral differences. Furthermore, we propose an automated search for neutral differences to address the problem of a limited search range of neutral differences, as previous approaches relied on experimental testing. This approach provides a basis for the subspace spanned by the neutral differences, and we apply this technique to both SPECK32 and LEA, where the predicted results closely match the experimental ones. Consequently, we present the improved differential-linear distinguishers for SPECK32 and LEA, along with the 18-round attacks on LEA192 and LEA256 with the lowest time complexity up to date.

Neighborhood based computational approaches for the prediction of lncRNA-disease associations

MotivationLong non-coding RNAs (lncRNAs) are a class of molecules involved in important biological processes. Extensive efforts have been provided to get deeper understanding of disease mechanisms at the lncRNA level, guiding towards the detection of biomarkers for disease diagnosis, treatment, prognosis and prevention. Unfortunately, due to costs and time complexity, the number of possible disease-related lncRNAs verified by traditional biological experiments is very limited. Computational approaches for the prediction of disease-lncRNA associations allow to identify the most promising candidates to be verified in laboratory, reducing costs and time consuming.ResultsWe propose novel approaches for the prediction of lncRNA-disease associations, all sharing the idea of exploring associations among lncRNAs, other intermediate molecules (e.g., miRNAs) and diseases, suitably represented by tripartite graphs. Indeed, while only a few lncRNA-disease associations are still known, plenty of interactions between lncRNAs and other molecules, as well as associations of the latters with diseases, are available. A first approach presented here, NGH, relies on neighborhood analysis performed on a tripartite graph, built upon lncRNAs, miRNAs and diseases. A second approach (CF) relies on collaborative filtering; a third approach (NGH-CF) is obtained boosting NGH by collaborative filtering. The proposed approaches have been validated on both synthetic and real data, and compared against other methods from the literature. It results that neighborhood analysis allows to outperform competitors, and when it is combined with collaborative filtering the prediction accuracy further improves, scoring a value of AUC equal to 0966.AvailabilitySource code and sample datasets are available at: https://github.com/marybonomo/LDAsPredictionApproaches.git

Do Implicit Learning Deficit and Dyslexia Go Together? An fMRI and Behavioral Study

AbstractWhat is the relationship between literacy skills and implicit learning? To address previous mixed findings, we compared school‐aged readers, typical (CON, n = 54) and with dyslexia (DYS, n = 53), in relation to their performance on a serial reaction time task. For the first time, we also included an isolated spelling deficit group (ISD, n = 30) to control for distinctive effects of reading and spelling deficits. A linear reaction times analysis did not reveal between‐group differences in implicit learning. However, further examination revealed that most CON (65%) and ISD (63%) were implicit learners, whereas most DYS were nonlearners (64%). Brain activity showed differences in early learning phases: CON learners and DYS nonlearners activated the left inferior frontal gyrus (IFG) and left insula more than other groups. Our findings imply that implicit learning is more frequently disrupted in children with dyslexia than in typical readers, and that activation of the left IFG and insula contributes to effective learning in the latter group but it does not in the former.

Bandit approach for unmanned aerial vehicle-centric low earth orbit satellite selection

In this paper, the problem of optimal low earth orbit satellite (LEO-Sat) selection by unmanned aerial vehicle (UAV) in space air ground integrated networks (SAGINs) will be investigated. The primary objective is to maximize the UAV's achievable data rate while ensuring a prolonged remaining visible time for the selected LEO-Sat. This problem poses significant challenges due to the highly dynamic nature caused by the substantial relative velocity between UAV and LEO-Sats, making LEO-Sats' features, e.g., elevation angle, remaining visible time, and the available bandwidth strongly time-dependent. Moreover, some LEO-Sats will appear/disappear from UAV visibility within short time intervals. An online learning approach will be proposed to efficiently address this highly dynamic problem, where it will be modeled as contextual multi-armed bandit (MAB) with dynamic arms. Through this modeling, the time varying LEO-Sats' features as well as their dynamic appearance/disappearance will be taken into account while selecting the best LEO-Sat. Through extensive numerical analysis, the performance of the proposed approach nearly matches that of the highly complex optimal performance based on exhaustive searching with negligible time complexity, and it outperforms conventional schemes that individually maximize one of the LEO-Sats' features.

Enhancing MQTT security for internet of things: Lightweight two-way authorization and authentication with advanced security measures

The MQTT (Message Queuing Telemetry Transport) protocol used the pub-sub model for IoT communication. Bolstering security with two-way authentication becomes a critical necessity. This paper presents a novel algorithm that fortifies MQTT with enhanced security measures, utilizing a refined MQTT implementation that integrates with a Merkle tree. HBMQTT Broker increases security as it uses different plugins, like the authentication plugin and the authorization plugin. These plugins serve to add extra layers of protection, reinforcing security protocols and heightening resilience to potential threats. The Merkle tree integration enhances data security during data transmission, effectively distinguishing between authentic and inauthentic data streams. Merkle trees generate tokens, which are used for secure data transmission. The algorithm is tested with four different hacking adversarial attacks: man-in-The-Middle (MITM), malware attack, denial of service (DoS), and phishing attack. Space and time complexity are also calculated for these attacks.

ОЦІНКА ОБЧИСЛЮВАЛЬНОЇ СКЛАДНОСТІ ГЕНЕТИЧНОГО АЛГОРИТМУ

The article is devoted to the estimation of computational complexity of a genetic algorithm as one of the key tools for solving optimisation problems. The theoretical aspects of computational complexity of algorithms and the interrelation of elements of a genetic algorithm are considered. The main types of computational complexity of algorithms are described: time, simple and asymptotic. Five basic rules for calculating the asymptotic complexity are given. A mathematical apparatus for estimating the asymptotic complexity of a genetic algorithm is presented, which takes into account the costs of forming the initial population and performing evolution. Evolution occurs through iterations, during which generations of individuals are subjected to certain operations in order to find an optimal solution (crossing, mutation, chromosome decoding, etc.). GA, as a global search algorithm, is considered to find the optimal path without getting stuck in local minima. To assess the computational complexity of GA, we consider solving the traveling salesman problem (TSP) for 28 cities of Ukraine using a modified TSPLIB library and the DEAP platform created in the Python programming language. A block diagram of the GA is presented, the main elements of which are the tournament selection operator, the ordered crossover operator, and the inversion mutation operator. The influence of the population size and the number of generations on the asymptotic complexity of the genetic algorithm in solving the TSP problem is studied. The study considered changing the size of the GA population from 50 to 500 with a step of 50, while for each such value four sets of the number of generations were modelled: from 50 to 200 with a step of 50. Based on the obtained results, we show a linear dependence of the GA execution time on the size of the considered input data. It is shown that the smallest time complexity of the presented GA for the given TSP problem is 0.33848 seconds with a population size of 50 and a similar number of generations, while the largest value is 3.752734 seconds with a population size of 500 and a number of generations of 200. The obtained results can be used to optimise the performance of a GA in the TSP problem.