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Purpose: The popularity of fencing and intense sports competition has burdened adolescents with excessive training, harming their immature bodies. Traditional training methods fail to provide timely movement corrections and personalized plans, leading to ineffective exercises. This paper aims to use artificial intelligence technology to reduce ineffective exercises and alleviate the training burden. Methods: We propose an action recognition algorithm based on the characteristics of adolescent athletes. This algorithm uses multimodal input data to comprehensively extract action information. Each modality is processed by the same network structure, utilizing attention mechanisms and adaptive graph structures. A multibranch feature fusion method is used to determine the final action category. Results: We gathered the fencing footwork data set 2.0. Our model achieved 93.3% accuracy, with the highest precision at 95.8% and the highest F1-Score at 94.5% across all categories. It effectively recognized actions of adolescents with different heights and speeds, outperforming traditional methods. Conclusion: Our artificial intelligence-based training solution improves training efficiency and reduces the training burden on adolescents. [ABSTRACT FROM AUTHOR]

We develop an algorithm for solving a large class of nonlinear high-dimensional continuous-time models in finance. We approximate value and policy functions using deep learning and show that a combination of automatic differentiation and Ito's lemma allows for the computation of exact expectations, resulting in a negligible computational cost that is independent of the number of state variables. We illustrate the applicability of our method to problems in asset pricing, corporate finance, and portfolio choice and show that the ability to solve high-dimensional problems allows us to derive new economic insights. [ABSTRACT FROM AUTHOR]

In molecular simulations, efficient methods for investigating equilibration and slow relaxation in dense systems are crucial yet challenging. This study focuses on the diffusional characteristics of monodisperse hard disk systems at equilibrium, comparing novel methodologies of event-chain Monte Carlo variants, specifically the Newtonian event-chain and straight event-chain algorithms. We systematically analyze both event-based and CPU time-based efficiency in liquid and solid phases, aiming to elucidate the microscopic mechanisms underlying structural relaxation. Our results demonstrate how chain length or duration, system size, and phase state influence the efficiency of diffusion dynamics, including hopping motion. This work provides insights into optimizing simulation techniques for highly packed systems and has the potential to improve our understanding of diffusion dynamics even in complex many-body systems. [ABSTRACT FROM AUTHOR]

Recent quantum algorithms pertaining to electronic structure theory primarily focus on the threshold-based dynamic construction of ansatz by selectively including important many-body operators. These methods can be made systematically more accurate by tuning the threshold to include a greater number of operators into the ansatz. However, such improvements come at the cost of rapid proliferation of the circuit depth, especially for highly correlated molecular systems. In this work, we address this issue by the development of a novel theoretical framework that relies on the segregation of an ansatz into a dynamically selected core "principal" component, which is, by construction, adiabatically decoupled from the remaining operators. This enables us to perform computations involving the principal component using extremely shallow-depth circuits, whereas the effect of the remaining "auxiliary" component is folded into the energy function via a cost-efficient non-iterative correction, ensuring the requisite accuracy. We propose a formalism that analytically predicts the auxiliary parameters from the principal ones, followed by a suite of non-iterative auxiliary subspace correction techniques with different levels of sophistication. The auxiliary subspace corrections incur no additional quantum resources yet complement an inadequately expressive core of the ansatz to recover a significant amount of electronic correlations. We have numerically validated the resource efficiency and accuracy of our formalism with a number of strongly correlated molecular systems. [ABSTRACT FROM AUTHOR]

This paper presents an improved two-dimensional logistic map. Relative to the original logistic map, the improved chaos map has better performance, e.g., larger chaotic range, higher complexity, and boosting sequence. Based on these good performances, a novel encipherment strategy is designed according to the hybrid coding of DNA and S-box algorithm. During the design of the algorithm, the primary operations include chaotic stream generation, permutation, and diffusion. The chaotic streams are constructed from the improved two-dimensional logistic map. The diffusion stage is achieved by the spatiotemporal chaos algorithm. The security test reflects that the design algorithm can effectively defend against external attacks. [ABSTRACT FROM AUTHOR]

In past decades, enormous effort has been expended to develop algorithms and even to construct special-purpose computers in order to efficiently evaluate total energies and forces for long-range-interacting particle systems, with the particle-mesh Ewald and the fast multipole methods as well as the "Anton" series of supercomputers serving as examples for biomolecular simulations. Cutoffs in the range of the interaction have also been used for large systems. All these methods require extrapolations. Within Markov-chain Monte Carlo, in thermal equilibrium, the Boltzmann distribution can, however, be sampled natively without evaluating the total energy. Using as an example the Lennard-Jones interaction, we review past attempts in this direction and then discuss in detail the class of cell-veto algorithms that allow for the fast, native sampling of the Boltzmann distribution without any approximation, extrapolation, or cutoff even for the slowly decaying Coulomb interaction. The computing effort per move remains constant with increasing system size, as we show explicitly. We provide worked-out illustrations and pseudocode representations of the discussed algorithms. Python scripts are made available in an associated open-source software repository. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive memetic algorithm based on a new neighbourhood structure (AMA) for solving the assembly job shop scheduling problem, with the aim of minimising the maximum completion time (makespan). To utilise the knowledge of problem, a theoretical analysis is conducted to explore the criteria for feasible and effective movement of operations under assembly constraints, and a multi-operation precise joint movement neighbourhood structure is proposed accordingly. In the AMA, to ensure the feasibility of solutions during the evolution process, a feasible encoding mechanism based on the constraint degree of operations is designed, a greedy active decoding method as well as feasible crossover operation based on independent operation chains are designed specifically for this encoding method. To avoid premature convergence of the population, a population update operator with diversity adaptive control is proposed. Finally, by comparing the results with five state-of-the-art algorithms, the superiority of AMA in terms of solution quality and stability is verified, particularly with the update of known optimal solutions for 11 instances. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has the potential to positively impact library and archives collections and services--enhancing reference, instruction, metadata creation, recommendations, and more. However, AI also has ethical implications. This paper presents an extensive literature and review analysis that examines AI projects implemented in library and archives settings, asking the following research questions: RQ1: How is artificial intelligence being used in libraries and archives practice? RQ2: What ethical concerns are being identified and addressed during AI implementation in libraries and archives? The results of this literature review show that AI implementation is growing in libraries and archives and that practitioners are using AI for increasingly varied purposes. We found that AI implementation was most common in large, academic libraries. Materials used in AI projects usually involved digitized and born digital text and images, though materials also ranged to include web archives, electronic theses and dissertations (ETDs), and maps. AI was most often used for metadata extraction and reference and research services. Just over half of the papers included in the literature review mentioned ethics or values related issues in their discussions of AI implementation in libraries and archives, and only one-third of all resources discussed ethical issues beyond technical issues of accuracy and human-in-the-loop. Case studies relating to AI in libraries and archives are on the rise, and we expect subsequent discussions of relevant ethics and values to follow suit, particularly growing in the areas of cost considerations, transparency, reliability, policy and guidelines, bias, social justice, user communities, privacy, consent, accessibility, and access. As AI comes into more common usage, it will benefit the library and archives professions to not only consider ethics when implementing local projects, but to publicly discuss these ethical considerations in shared documentation and publications. [ABSTRACT FROM AUTHOR]

In the field of higher education, generative artificial intelligence (GenAI) has become a revolutionary influence, shaping how students access and use library resources. This study explores the intricate balance of both positive and negative effects that GenAI might have on the academic library experience for higher education (HE) students. The key aspects of enhanced discovery and retrieval, personalization and engagement, streamlined research processes, and digital literacy and information evaluation potentially offered through using generative AI will be considered. These prospective advantages to HE students offered by using GenAI will be examined through will be examined through the theoretical framework of the Technological Acceptance Model (TAM) introduced by Davis et al. in 1986, which suggests that perceived usefulness and perceived ease of use are key factors in determining user acceptance and utilization of technology. The adoption of GenAI by higher education students will be analyzed from this viewpoint before assessing its impact on their use of library resources. [ABSTRACT FROM AUTHOR]

For the past 70 years, researchers and experimental musicians have been working with computersynthesized music, forming a collaborative relationship with generative artificial intelligences known as human--AI co-creation. The last several years have shown that musical artists are quickly adopting AI tools to produce music for AI music competitions and for commercial production of songs and albums. The United States Copyright Office, in response to this trend, has released its latest policy revisions to clearly define what is eligible for copyright registration. Soon after, the Program for Cooperative Cataloging (PCC) also released new guidelines, providing recommendations for how library catalogers should address AI-generated materials. In both cases, they reject the notion of considering AI as a contributor. The language in each of these policies, however, is self-contradicting, showing that they are ill equipped to address generative AI. This study leverages critical textual analysis and qualitative content analysis and uses case examples to probe the manner in which these policies regard generative AI. Recommendations are made for addressing shortcomings in the PCC's policies, and moral philosophical frameworks such as virtue ethics and consequentialism support arguments for supplementing catalog item records with information from authoritative external sources, deviating from this policy for the sake of truth-seeking. [ABSTRACT FROM AUTHOR]

We employ the exact-factorization formalism to study the coupled dynamics of photons, electrons, and nuclei at the quantum mechanical level, proposing illustrative examples of model situations of nonadiabatic dynamics and spontaneous emission of electron–nuclear systems in the regime of strong light–matter coupling. We make a particular choice of factorization for such a multi-component system, where the full wavefunction is factored as a conditional electronic amplitude and a marginal photon–nuclear amplitude. Then, we apply the coupled-trajectory mixed quantum–classical (CTMQC) algorithm to perform trajectory-based simulations, by treating photonic and nuclear degrees of freedom on equal footing in terms of classical-like trajectories. The analysis of the time-dependent potentials of the theory along with the assessment of the performance of CTMQC allows us to point out some limitations of the current approximations used in CTMQC. Meanwhile, comparing CTMQC with other trajectory-based algorithms, namely multi-trajectory Ehrenfest and Tully surface hopping, demonstrates the better quality of CTMQC predictions. [ABSTRACT FROM AUTHOR]

Although selected configuration interaction (SCI) algorithms can tackle much larger Hilbert spaces than the conventional full CI method, the scaling of their computational cost with respect to the system size remains inherently exponential. In addition, inaccuracies in describing the correlation hole at small interelectronic distances lead to the slow convergence of the electronic energy relative to the size of the one-electron basis set. To alleviate these effects, we show that the non-Hermitian, transcorrelated (TC) version of SCI significantly compactifies the determinant space, allowing us to reach a given accuracy with a much smaller number of determinants. Furthermore, we note a significant acceleration in the convergence of the TC-SCI energy as the basis set size increases. The extent of this compression and the energy convergence rate are closely linked to the accuracy of the correlation factor used for the similarity transformation of the Coulombic Hamiltonian. Our systematic investigation of small molecular systems in increasingly large basis sets illustrates the magnitude of these effects. [ABSTRACT FROM AUTHOR]

We examine an algorithm for the creation of proton-disordered ice cells based on a simulated-annealing (SA) scheme for molecular orientations. Application to defect-free ice Ih, a clathrate-hydrate structure, and a random polycrystalline ice Ih sample demonstrates the SA technique to be effective, attaining maximum HB connectivity using relatively short cooling simulations, thus serving as an alternative method for those cases in which the application of topology-based methods is inhibited. [ABSTRACT FROM AUTHOR]

In this paper, we report a new algorithm for creating an adaptive basis set in the Multiconfigurational Ehrenfest (MCE) method, which is termed Full Cloning (FC), and test it together with the existing Multiple Cloning (MC) using the spin-boson model at zero-temperature as a benchmark. The zero-temperature spin-boson regime is a common hurdle in the development of methods that seek to model quantum dynamics. Two versions of MCE exist. We demonstrate that MC is vital for the convergence of MCE version 2 (MCEv2). The first version (MCEv1) converges much better than MCEv2, but FC improves its convergence in a few cases where it is hard to converge it with the help of a reasonably small size of the basis set. [ABSTRACT FROM AUTHOR]

Multiple algorithms exist for calculating Coulomb (J) or exchange (K) contributions to Fock-like matrices, and it is beneficial to develop a framework that allows the seamless integration and combination of different J and K construction algorithms. In Psi4, we have implemented the "CompositeJK" formalism for this purpose. CompositeJK allows for the combination of any J and K construction algorithms for any quantum chemistry method formulated in terms of J-like or K-like matrices (including, but not limited to, Hartree–Fock and density functional theory) in a highly modular and intuitive fashion, which is simple to utilize for both developers and users. Using the CompositeJK framework, Psi4 was interfaced to the sn-LinK implementation in the GauXC library, adding the first instance of noncommercial graphics processing unit (GPU) support for the construction of Fock matrix elements to Psi4. On systems with hundreds of atoms, the interface to the CPU sn-LinK implementation displays a higher performance than all the alternative JK construction methods available in Psi4, with up to x2.8 speedups compared to existing Psi4JK implementations. The GPU sn-LinK implementation, harnessing the power of GPUs, improves the observed performance gains to up to x7.0. [ABSTRACT FROM AUTHOR]

Here, we establish an approach to determine temperature-dependent aggregation rates in terms of thermostatistical quantities, which can be obtained directly from flat-histogram and statistical temperature algorithms considering the density of states of the system. Our approach is validated through simulations of an Ising-like model with anisotropically interacting particles at temperatures close to its first-order phase transition. Quantitative comparisons between the numerically obtained forward and reverse rates to approximate analytical expressions corroborate its use as a model-independent approach. [ABSTRACT FROM AUTHOR]

Log-concave sampling has witnessed remarkable algorithmic advances in recent years, but the corresponding problem of proving lower bounds for this task has remained elusive, with lower bounds previously known only in dimension one. In this work, we establish the following query lower bounds: (1) sampling from strongly log-concave and log-smooth distributions in dimension \(d\ge 2\) requires \(\Omega (\log \kappa)\) queries, which is sharp in any constant dimension, and (2) sampling from Gaussians in dimension d (hence also from general log-concave and log-smooth distributions in dimension d) requires \(\widetilde{\Omega }(\min (\sqrt \kappa \log d, d))\) queries, which is nearly sharp for the class of Gaussians. Here, \(\kappa\) denotes the condition number of the target distribution. Our proofs rely upon (1) a multiscale construction inspired by work on the Kakeya conjecture in geometric measure theory, and (2) a novel reduction that demonstrates that block Krylov algorithms are optimal for this problem, as well as connections to lower bound techniques based on Wishart matrices developed in the matrix-vector query literature. [ABSTRACT FROM AUTHOR]

For a finite set of balls of radius r, the k-fold cover is the space covered by at least k balls. Fixing the ball centers and varying the radius, we obtain a nested sequence of spaces that is called the k-fold filtration of the centers. For k=1, the construction is the union-of-balls filtration that is popular in topological data analysis. For larger k, it yields a cleaner shape reconstruction in the presence of outliers. We contribute a sparsification algorithm to approximate the topology of the k-fold filtration. Our method is a combination and adaptation of several techniques from the well-studied case k=1, resulting in a sparsification of linear size that can be computed in expected near-linear time with respect to the number of input points. Our method also extends to the multicover bifiltration, composed of the k-fold filtrations for several values of k, with the same size and complexity bounds. [ABSTRACT FROM AUTHOR]

This study aimed to clarify the differences in muscular coordination during butterfly swimming between high- and low-performance swimmers using muscle synergy analysis. Eight female competitive swimmers and 8 female recreational swimmers participated in this study. The participants swam a 25-m butterfly stroke with maximum effort. Surface electromyography was measured from 12 muscles and muscle synergy analysis was performed from the data using nonnegative matrix factorization algorithms. From the results of the muscle synergy analysis, 4 synergies were extracted from both groups. Synergies 1 and 2 were characterized by coactivation of the upper and lower limb muscles in the recreational swimmers, whereas only synergy 1 was characterized by this in the competitive swimmers. Synergy 3 was involved in arm recovery in both groups. Synergy 4 was only involved in the downward kick in the competitive swimmers. From these results, it can be concluded that muscle synergies with combined coordination of upper and lower limb muscles were extracted more in the recreational swimmers and that the competitive swimmers controlled the downward kick with an independent synergy and that the adjustment of the timing of the downward kick may be an important factor for the efficient performance of butterfly swimming. [ABSTRACT FROM AUTHOR]

Sequential optimization is one of the promising approaches in identifying the optimal candidate(s) (molecules, reactants, drugs, etc.) with desired properties (reaction yield, selectivity, efficacy, etc.) from a large set of potential candidates, while minimizing the number of experiments required. However, the high dimensionality of the feature space (e.g., molecular descriptors) makes it often difficult to utilize the relevant features during the process of updating the set of candidates to be examined. In this article, we developed a new sequential optimization algorithm for molecular problems based on reinforcement learning, multi-armed linear bandit framework, and online, dynamic feature selections in which relevant molecular descriptors are updated along with the experiments. We also designed a stopping condition aimed to guarantee the reliability of the chosen candidate from the dataset pool. The developed algorithm was examined by comparing with Bayesian optimization (BO), using two synthetic datasets and two real datasets in which one dataset includes hydration free energy of molecules and another one includes a free energy difference between enantiomer products in chemical reaction. We found that the dynamic feature selection in representing the desired properties along the experiments provides a better performance (e.g., time required to find the best candidate and stop the experiment) as the overall trend and that our multi-armed linear bandit approach with a dynamic feature selection scheme outperforms the standard BO with fixed feature variables. The comparison of our algorithm to BO with dynamic feature selection is also addressed. [ABSTRACT FROM AUTHOR]

Algorithmic HR systems are becoming a more prevalent interface between organizations and employees. Yet little research has examined how automated HR processes impact employee motivation. In a three‐wave study (NTime1 = 401; NTime2 = 379; NTime3 = 303), we investigated the motivational effects of HR systems that automatically capture—and make decisions based on—employee performance, and whether these effects depend on employee attributions regarding the organization's intended use of its automated HR metric system. Additionally, we test whether these motivational states affect employee task prioritization and emotional exhaustion. Results show that employees whose organizations use algorithmic HR systems, and who also attribute managerial control as intent to that system, experience higher levels of extrinsic motivation at work. This, in turn, predicts greater prioritization of metricized tasks and de‐prioritization of non‐metricized tasks. Conversely, employees who believe the purpose of algorithmic HR systems is to provide them with constructive feedback are more likely to experience intrinsic motivation, leading to reduced feelings of emotional exhaustion, greater prioritization of metricized tasks, but reduced non‐metricized behavior de‐prioritization. These results illustrate the critical importance of employee sensemaking around algorithmic HR systems as a precursor to the impact of such systems on employee motivation, behavior, and well‐being. [ABSTRACT FROM AUTHOR]

It is a fundamental problem to understand the complexity of high-accuracy sampling from a strongly log-concave density π on ℝd. Indeed, in practice, high-accuracy samplers such as the Metropolis-adjusted Langevin algorithm (MALA) remain the de facto gold standard; and in theory, via the proximal sampler reduction, it is understood that such samplers are key for sampling even beyond log-concavity (in particular, for sampling under isoperimetric assumptions). This article improves the dimension dependence of this sampling problem to \(\widetilde{O}(d^{1/2})\). The previous best result for MALA was \(\widetilde{O}(d)\). This closes the long line of work on the complexity of MALA and, moreover, leads to state-of-the-art guarantees for high-accuracy sampling under strong log-concavity and beyond (thanks to the aforementioned reduction). Our starting point is that the complexity of MALA improves to \(\widetilde{O}(d^{1/2})\) , but only under a warm start (an initialization with constant Rényi divergence w.r.t. π). Previous algorithms for finding a warm start took O(d) time and thus dominated the computational effort of sampling. Our main technical contribution resolves this gap by establishing the first \(\widetilde{O}(d^{1/2})\) Rényi mixing rates for the discretized underdamped Langevin diffusion. For this, we develop new differential-privacy-inspired techniques based on Rényi divergences with Orlicz–Wasserstein shifts, which allow us to sidestep longstanding challenges for proving fast convergence of hypocoercive differential equations. [ABSTRACT FROM AUTHOR]

Time series biomechanical data inform our understanding of normal gait mechanics and pathomechanics. This study examines the utility of different quantitative methods to distinguish vertical ground reaction forces (VGRFs) from experimentally distinct gait strategies. The goals of this study are to compare measures of VGRF data—using the shape factor method and a Fourier series-based analysis—to (1) describe how these methods reflect and distinguish gait patterns and (2) determine which Fourier series coefficients discriminate normal walking, with a relatively stiff-legged gait, from compliant walking, using deep knee flexion and limited vertical oscillation. This study includes a reanalysis of previously presented VGRF data. We applied the shape factor method and fit 3- to 8-term Fourier series to zero-padded VGRF data. We compared VGRF renderings using Euclidean L2 distances and correlations stratified by gait strategy. Euclidean L2 distances improved with additional harmonics, with limited improvement after the seventh term. Euclidean L2 distances were greater in shape factor versus Fourier series renderings. In the 8 harmonic model, amplitudes of 9 Fourier coefficients—which contribute to VGRF features including peak and local minimum amplitudes and limb loading rates—were different between normal and compliant walking. The results suggest that Fourier series-based methods distinguish between gait strategies. [ABSTRACT FROM AUTHOR]

Sire is a Python/C++ library that is used both to prototype new algorithms and as an interoperability engine for exchanging information between molecular simulation programs. It provides a collection of file parsers and information converters that together make it easier to combine and leverage the functionality of many other programs and libraries. This empowers researchers to use sire to write a single script that can, for example, load a molecule from a PDBx/mmCIF file via Gemmi, perform SMARTS searches via RDKit, parameterize molecules using BioSimSpace, run GPU-accelerated molecular dynamics via OpenMM, and then display the resulting dynamics trajectory in a NGLView Jupyter notebook 3D molecular viewer. This functionality is built on by BioSimSpace, which uses sire's molecular information engine to interconvert with programs such as GROMACS, NAMD, Amber, and AmberTools for automated molecular parameterization and the running of molecular dynamics, metadynamics, and alchemical free energy workflows. Sire comes complete with a powerful molecular information search engine, plus trajectory loading and editing, analysis, and energy evaluation engines. This, when combined with an in-built computer algebra system, gives substantial flexibility to researchers to load, search for, edit, and combine molecular information from multiple sources and use that to drive novel algorithms by combining functionality from other programs. Sire is open source (GPL3) and is available via conda and at a free Jupyter notebook server at https://try.openbiosim.org. Sire is supported by the not-for-profit OpenBioSim community interest company. [ABSTRACT FROM AUTHOR]

This research investigates the impact of algorithmic management on worker behaviors, focusing on workers' commitment to service quality and referral tendencies. Drawing upon the job demands‐resources model, we argue that high levels of algorithmic management could create hindrance demands that impede service quality and demotivate referral behaviors. We propose that high workload, as a challenge demand, buffers the negative effects of algorithmic management on worker outcomes. We find support for our proposed research model in an experiment with a sample of 1362 platform‐based food‐delivery riders. We also conduct a qualitative study with 21 riders, which provides a more nuanced understanding of how algorithmic management affects workers' attitudes, behaviors, and referral tendencies. [ABSTRACT FROM AUTHOR]

This paper reflects on the paradigmatic assumptions and ideologies that have shaped algorithmic management research. We identify two sets of assumptions: one about the "ontology of algorithms" (which holds that human resource management [HRM] algorithms are non‐human entities with material agency) and one about the "ontology of management" that HRM algorithms afford (which understands algorithmic management as a form of control for maximizing economic/shareholder value). We explain how these core assumptions underpin existing research of HRM algorithms, causing blind spots that hinder new ways of understanding and studying algorithmic management. After identifying and unpacking the assumptions and blind spots, we offer avenues to overcome these blind spots, allowing for future research based on new ideological assumption grounds that will help move algorithmic management scholarship further in significant ways. [ABSTRACT FROM AUTHOR]

Purpose: The study had 3 purposes: (1) to develop an index using machine-learning techniques to predict the fitness status of soccer players, (2) to explore the index's validity and its relationship with a submaximal run test (SMFT), and (3) to analyze the impact of weekly training load on the index and SMFT outcomes. Methods: The study involved 50 players from an Italian professional soccer club. External and internal loads were collected during training sessions. Various machine-learning algorithms were assessed for their ability to predict heart-rate responses during the training drills based on external load data. The fitness index, calculated as the difference between actual and predicted heart rates, was correlated with SMFT outcomes. Results: Random forest regression (mean absolute error = 3.8 [0.05]) outperformed the other machine-learning algorithms (extreme gradient boosting and linear regression). Average speed, minutes from the start of the training session, and the work:rest ratio were identified as the most important features. The fitness index displayed a very large correlation (r =.70) with SMFT outcomes, with the highest result observed during possession games and physical conditioning exercises. The study revealed that heart-rate responses from SMFT and the fitness index could diverge throughout the season, suggesting different aspects of fitness. Conclusions: This study introduces an "invisible monitoring" approach to assess soccer player fitness in the training environment. The developed fitness index, in conjunction with traditional fitness tests, provides a comprehensive understanding of player readiness. This research paves the way for practical applications in soccer, enabling personalized training adjustments and injury prevention. [ABSTRACT FROM AUTHOR]

Quantum chemistry is one of the most promising applications for which quantum computing is expected to have a significant impact. Despite considerable research in the field of electronic structure, calculating the vibrational properties of molecules on quantum computers remains a relatively unexplored field. In this work, we develop a vibrational Adaptive Derivative-Assembled Pseudo-Trotter Variational Quantum Eigensolver (vADAPT-VQE) formalism based on an infinite product representation (IPR) of anti-Hermitian excitation operators of the Full Vibrational Configuration Interaction (FVCI) wavefunction, which allows for preparing eigenstates of vibrational Hamiltonians on quantum computers. In order to establish the vADAPT-VQE algorithm using the IPR, we study the exactness of disentangled Unitary Vibrational Coupled Cluster (dUVCC) theory and show that dUVCC can formally represent the FVCI wavefunction in an infinite expansion. To investigate the performance of the vADAPT-VQE algorithm, we numerically study whether the vADAPT-VQE algorithm generates a sequence of operators that may represent the FVCI wavefunction. Our numerical results indicate frequent appearance of critical points in the wavefunction preparation using vADAPT-VQE. These results imply that one may encounter diminishing usefulness when preparing vibrational wavefunctions on quantum computers using vADAPT-VQE and that additional studies are required to find methods that can circumvent this behavior. [ABSTRACT FROM AUTHOR]

Recursive queries have been traditionally studied in the framework of datalog, a language that restricts recursion to monotone queries over sets, which is guaranteed to converge in polynomial time in the size of the input. But modern big data systems require recursive computations beyond the Boolean space. In this article, we study the convergence of datalog when it is interpreted over an arbitrary semiring. We consider an ordered semiring, define the semantics of a datalog program as a least fixpoint in this semiring, and study the number of steps required to reach that fixpoint, if ever. We identify algebraic properties of the semiring that correspond to certain convergence properties of datalog programs. Finally, we describe a class of ordered semirings on which one can use the semi-naïve evaluation algorithm on any datalog program. [ABSTRACT FROM AUTHOR]

Purpose: This preliminary study examined the influence of estimated menstrual-cycle (MC) phase on responses to soccer matches and training sessions in preparation for and during the FIFA (Fédération internationale de football association) Women's World Cup 2019. Methods: Twenty outfield players representing a national team were tracked over a 45-day period. External (10-Hz global positioning system; total and distance covered at high-metabolic power [≥20 W·kg−1]) and internal load measures (minutes ≥80% heart-rate maximum, sessional ratings of perceived exertion) were collected during all training and matches, with single-item wellness measures (fatigue, soreness, sleep quality, and sleep duration) collected each morning prior to activity. MC phase was estimated individually via an algorithm, informed from pretournament survey responses and ongoing symptom reporting (FitrWoman). Model comparison statistics were used to determine the impact of estimated MC phase in nonhormonal contraceptive users (n = 16). Results: Sessional rating of perceived exertion responses to total distances ≥5 km were higher during the luteal phase (+0.6–1.0 au; P ≤.0178) versus menstruation (phase 1), but no other observable dose–response trends were observed. Sleep, fatigue, and soreness ratings were not typically associated with MC phase, with the exception of exacerbated fatigue ratings in luteal versus follicular phase 48 hours postmatch (−0.73 au, P =.0275). Conclusions: Preliminary findings suggest that estimated MC phase may contribute to the understanding of the dose–response to soccer training and matches. [ABSTRACT FROM AUTHOR]

Within the context of fewest-switch surface hopping (FSSH) dynamics, one often wishes to remove the angular component of the derivative coupling between states J and K . In a previous set of papers, Shu et al. [J. Phys. Chem. Lett. 11, 1135–1140 (2020)] posited one approach for such a removal based on direct projection, while we isolated a second approach by constructing and differentiating a rotationally invariant basis. Unfortunately, neither approach was able to demonstrate a one-electron operator O ̂ whose matrix element J O ̂ K was the angular component of the derivative coupling. Here, we show that a one-electron operator can, in fact, be constructed efficiently in a semi-local fashion. The present results yield physical insight into designing new surface hopping algorithms and are of immediate use for FSSH calculations. [ABSTRACT FROM AUTHOR]

Inspired by real-life applications, mainly in hand-intensive manufacturing, the incorporation of learning effects into scheduling problems has garnered attention in recent years. This paper deals with the flowshop scheduling problem with a learning effect, when minimising the makespan. Four approaches to model the learning effect, well-known in the literature, are considered. Mathematical models are providing for each case. A solver allows us to find the optimal solution in small problem instances, while a Simulated Annealing algorithm is proposed to deal with large problem instances. In the latter, the initial solution is obtained using the well-known Nawaz-Enscore-Ham algorithm, and two local search operators are evaluated. Computational experiments are carried out using benchmark datasets from the literature. The Simulated Annealing algorithm shows a better result for learning approaches with fast learning effects as compared to slow learning effects. Finally, for industrial decision makers, some insights about how the learning effect model might affect the makespan minimisation flowshop scheduling problem are presented. [ABSTRACT FROM AUTHOR]

This paper addresses the semi-online scheduling problem of minimising the total weighted completion time on a single machine, where a combination of information on jobs release dates and processing times is considered. In this study, jobs can only arrive at known future times and a lower bound on jobs processing times is known in advance. A new semi-online algorithm is presented and is shown to be the best possible for the considered problem. In order to make this statement, a new lower bound on the competitive ratio of any semi-online algorithm for the problem is developed and, using competitive analysis, the proposed semi-online algorithm is shown to have a competitive ratio that matches the lower bound. [ABSTRACT FROM AUTHOR]

Ice is different from ordinary crystals because it contains randomness, which means that statistical treatment based on ensemble averaging is essential. Ice structures are constrained by topological rules known as the ice rules, which give them unique anomalous properties. These properties become more apparent when the system size is large. For this reason, there is a need to produce a large number of sufficiently large crystals that are homogeneously random and satisfy the ice rules. We have developed an algorithm to quickly generate ice structures containing ions and defects. This algorithm is provided as an independent software module that can be incorporated into crystal structure generation software. By doing so, it becomes possible to simulate ice crystals on a previously impossible scale. [ABSTRACT FROM AUTHOR]

The article discusses the negative implications of an essay-writing algorithm called Essay Genius. The authors, Dirk Lindebaum and Manuel F. Ramirez, express concern about the availability of this technology, which undermines student achievement, the work of educators, and trust in higher education. They explain how the algorithm works and provide practical recommendations to deter its use for cheating. Additionally, they suggest using algorithmic outputs as a pedagogical tool to prompt discussions about automation and the value of human-generated work. The authors emphasize the need for immediate attention to address the potential damage to public trust in higher education. [Extracted from the article]

Materials incorporating covalent adaptive networks (CAN), e.g., vitrimers, have received significant scientific attention due to their distinctive attributes of self-healing and stimuli-responsive properties. Different from direct crosslinked systems, bivalent and multivalent systems require a bond swap algorithm that respects detailed balance, considering the multiple equilibria in the system. Here, we propose a simple and robust algorithm to handle bond swap in multivalent and multi-species CAN systems. By including a bias term in the acceptance of Monte Carlo moves, we eliminate the imbalance from the bond swap site selection and multivalency effects, ensuring the detailed balance for all species in the system. [ABSTRACT FROM AUTHOR]

Advanced digital technologies have rapidly been integrated into prosthodontics to improve the digital workflow for prosthetic rehabilitation. The integration of 3D datasets acquired from various imaging sources such as intraoral scanners and facial optical scanners allows the creation of virtual patients to perform presurgical simulation and prosthetic rehabilitation. The presented technique introduced a straightforward protocol for aligning intraoral scans (Trios 4, 3Shape) to optical face scans (Face Hunter, Zirkonzahn) using a global best-fit algorithm of 3D evaluation software (GOM Inspect). Nasal geometry data were used as the matching reference to produce virtual dental patients. This integration protocol ensured that the intraoral scanner (Trios 4, 3Shape) was used not only to scan dental arches but also used effectively to scan the nose. These scans along with professional facial scans can be successfully aligned to produce virtual dental patients. As only a single fully dentate patient case with an alignment deviation of 243.6 µm was used, further research to evaluate the accuracy of this protocol is needed. [ABSTRACT FROM AUTHOR]

We study equi-join computation in the massively parallel computation (MPC) model. Currently, a main open question under this topic is whether it is possible to design an algorithm that can process any join with load O(N polylog N/p1/ρ*) — measured in the number of words communicated per machine — where N is the total number of tuples in the input relations, ρ* is the join's fractional edge covering number, and p is the number of machines. We settle the question in the negative for the class of tuple-based algorithms (all the known MPC join algorithms fall in this class) by proving the existence of a join query with ρ* = 2 that requires a load of Ω (N/p1/3) to evaluate. Our lower bound provides solid evidence that the "AGM bound" alone is not sufficient for characterizing the hardness of join evaluation in MPC (a phenomenon that does not exist in RAM). The hard join instance identified in our argument is cyclic, which leaves the question of whether O(N polylog N/p1/ρ*) is still possible for acyclic joins. We answer this question in the affirmative by showing that any acyclic join can be evaluated with load O(N / p1/ρ*), which is asymptotically optimal (there are no polylogarithmic factors in our bound). The separation between cyclic and acyclic joins is yet another phenomenon that is absent in RAM. Our algorithm owes to the discovery of a new mathematical structure — we call "canonical edge cover" — of acyclic hypergraphs, which has numerous non-trivial properties and makes an elegant addition to database theory. [ABSTRACT FROM AUTHOR]

Nearly thirty years ago, it was shown that \(\Omega (\sqrt {n})\) read/write registers are needed to solve randomized wait-free consensus among n processes. This lower bound was improved to n registers in 2018, which exactly matches known algorithms. The \(\Omega (\sqrt {n})\) space complexity lower bound actually applies to a class of objects called historyless objects, which includes registers, test-and-set objects, and readable swap objects. However, every known n-process obstruction-free consensus algorithm from historyless objects uses Ω (n) objects. In this paper, we give the first Ω (n) space complexity lower bounds on consensus algorithms for two kinds of historyless objects. First, we show that any obstruction-free consensus algorithm from swap objects uses at least n-1 objects. More generally, we prove that any obstruction-free k-set agreement algorithm from swap objects uses at least \(\lceil \frac{n}{k}\rceil - 1\) objects. The k-set agreement problem is a generalization of consensus in which processes agree on no more than k different output values. This is the first non-constant lower bound on the space complexity of solving k-set agreement with swap objects when k > 1. We also present an obstruction-free k-set agreement algorithm from n-k swap objects, which exactly matches our lower bound when k=1. Second, we show that any obstruction-free binary consensus algorithm from readable swap objects with domain size b uses at least \(\frac{n-2}{3b+1}\) objects. When b is a constant, this asymptotically matches the best known obstruction-free consensus algorithms from readable swap objects with unbounded domains. Since any historyless object can be simulated by a readable swap object with the same domain, our results imply that any obstruction-free consensus algorithm from historyless objects with domain size b uses at least \(\frac{n-2}{3b+1}\) objects. For b = 2, we show a slightly better lower bound of n-2. There is an obstruction-free binary consensus algorithm using 2n-1 readable swap objects with domain size 2, asymptotically matching our lower bound. [ABSTRACT FROM AUTHOR]

We study consensus in a protocol capturing in a simplified manner the major features of the majority of Proof of Stake blockchains. A committee is formed; one member proposes a block; and the others can check its validity and vote for it. Blocks with a majority of votes are produced. When an invalid block is produced, the stakes of the members who voted for it are "slashed." Profit-maximizing members interact with adversaries seeking to disrupt consensus. When slashing is limited, free-riding and moral-hazard lead to invalid blocks in equilibrium. We propose a protocol modification producing only valid blocks in equilibrium. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

The use of conventional scan bodies (SBs) with an intraoral scanner (IOS) to capture the position of a complete arch of dental implants has proven to be challenging. The literature is unclear about the accuracy of intraoral scanning techniques using SBs that are connected vertically to multiunit abutments (MUAs) for numerous adjacent implants in the same arch. Recently, there has been a paradigm shift from vertical SBs to horizontal SBs, which are positioned perpendicular to the long axis of the MUAs. Most IOSs available today can capture these horizontal SBs, called scan gauges (SGs), with better accuracy and consequently acquire the position of multiple adjacent implants using an effective scan path, thus reducing stitching and the number of images. The key to implementing this novel technology is to strategically arrange the SGs to optimize horizontal overlap of multiple adjacent SGs without touching each other. By superimposing two high-resolution intraoral scans of the SGs, an artificial intelligence (AI) algorithm is employed to produce a calibrated digital best-fit model on which a passive complete-arch prosthesis can be designed and fabricated. The advantages and disadvantages of SBs and SGs are discussed, and a case report using a digital workflow is presented. [ABSTRACT FROM AUTHOR]

There is an increasing body of research on algorithmic management (AM), but the field lacks measurement tools to capture workers' experiences of this phenomenon. Based on existing literature, we developed and validated the algorithmic management questionnaire (AMQ) to measure the perceptions of workers regarding their level of exposure to AM. Across three samples (overall n = 1332 gig workers), we show the content, factorial, discriminant, convergent, and predictive validity of the scale. The final 20‐item scale assesses workers' perceived level of exposure to algorithmic: monitoring, goal setting, scheduling, performance rating, and compensation. These dimensions formed a higher order construct assessing overall exposure to algorithmic management, which was found to be, as expected, negatively related to the work characteristics of job autonomy and job complexity and, indirectly, to work engagement. Supplementary analyses revealed that perceptions of exposure to AM reflect the objective presence of AM dimensions beyond individual variations in exposure. Overall, the results suggest the suitability of the AMQ to assess workers' perceived exposure to algorithmic management, which paves the way for further research on the impacts of these rapidly accelerating systems. [ABSTRACT FROM AUTHOR]

Coupled trajectory mixed quantum–classical (CTMQC) dynamics is a rigorous approach to trajectory-based non-adiabatic dynamics, which has recently seen an improvement to energy conservation via the introduction of the CTMQC-E algorithm. Despite this, the method's two key quantities distinguishing it from Ehrenfest dynamics, the modified Born–Oppenheimer momentum and the quantum momentum, require regularization procedures in certain circumstances. Such procedures in the latter can cause instabilities, leading to undesirable effects, such as energy drift and spurious population transfer, which is expected to become increasingly prevalent when the system gets larger as such events would happen more frequently. We propose a further modification to CTMQC-E, which includes a redefinition of the quantum momentum, CTMQC-EDI (double intercept), such that it has no formal divergences. We then show for Tully models I–III and the double arch model that the algorithm has greatly improved total energy conservation and negligible spurious population transfer at all times, in particular in regions of strong non-adiabatic coupling. CTMQC-EDI, therefore, shows promise as a numerically robust non-adiabatic dynamics technique that accounts for decoherence from first principles and that is scalable to large molecular systems and materials. [ABSTRACT FROM AUTHOR]

A novel implementation of the coupled-cluster singles and doubles (CCSD) approach is presented that is specifically tailored for the treatment of large symmetric systems. It fully exploits Abelian point-group symmetry and the use of the Cholesky decomposition of the two-electron repulsion integrals. In accordance with modern CCSD algorithms, we propose two alternative strategies for the computation of the so-called particle–particle ladder term. The code is driven toward the optimal choice depending on the available hardware resources. As a large-scale application, we computed the frozen-core correlation energy of buckminsterfullerene (C60) with a polarized valence triple-zeta basis set (240 correlated electrons in 1740 orbitals). [ABSTRACT FROM AUTHOR]

In order to further improve the security and efficiency of quantum image encryption, a three-layer encryption scheme for quantum color images based on 6D hyperchaos is proposed for the improved quantum representation model. The sequences generated by the 6D hyperchaotic system are applied to each layer of image encryption as keys. First, the quantum Fibonacci matrix is used to scramble the pixel value positions of the image to achieve the spatial scrambling of the quantum image; then, the position exchange of red, green, and blue channels and the bit-level cycle shift of pixel value are carried out alternately to realize the bit-level scrambling of quantum images; Finally, the XOR operation is performed on the quantum image whose position has been scrambled to realize the bit-level diffusion of the quantum image and obtain the final encrypted image. Simulation results show that the encryption algorithm is highly sensitive to the initial key, and the pixel values of the encrypted image are evenly distributed. In addition, quantum circuits are designed to implement the image encryption steps of each layer. [ABSTRACT FROM AUTHOR]

Separating the Coulomb potential into short-range and long-range components enables the use of different electron repulsion integral algorithms for each component. The short-range part can be efficiently computed using the analytical algorithm due to the locality in both the Gaussian-type orbital basis and the short-range Coulomb potentials. The integrals for the long-range Coulomb potential can be approximated with the density fitting method. A very small auxiliary basis is sufficient for the density fitting method to accurately approximate the long-range integrals. This feature significantly reduces the computational efforts associated with the N4 scaling in density fitting algorithms. For large molecules, the range separation and long-range density fitting method outperforms the conventional analytical integral evaluation scheme employed in Hartree–Fock calculations and provides more than twice the overall performance. In addition, this method offers a higher accuracy compared to conventional density fitting methods. The error in the Hartree–Fock energy can be easily reduced to 0.1 μEh per atom or smaller. [ABSTRACT FROM AUTHOR]

Developing accurate and computationally efficient algorithms for system performance metrics calculation is critical to implementing effective control and optimization in manufacturing system operations. In this paper, we propose a recursive decomposition/aggregation-based method for calculating the performance metrics of assembly/disassembly systems with multiple merge/split operations and sub-assemblies. It is assumed that the machines follow the exponential reliability model and the buffers are of finite capacity. To achieve this, we first consider assembly systems with multiple component lines merging at a single assembly operation. By decomposing the system into a set of virtual serial lines, we derive an analytical procedure to approximate the starvation and blockage probabilities of the merge operation, which are used to recursively update the parameters of the virtual serial lines. Then, the performance metrics of the original assembly system are approximated based on the corresponding machines and buffers in these virtual serial lines. Next, we extend the algorithm to assembly/disassembly systems with multiple merge/split operations and sub-assemblies. This is accomplished by identifying the so-called assembly/disassembly units formed based on the virtual serial lines and applying the calculations derived earlier recursively. Simulation experiments are carried out to justify the convergence, computational efficiency, and approximation accuracy of the proposed algorithms. An industrial case study is presented to demonstrate the theoretical methods in practical applications. [ABSTRACT FROM AUTHOR]

The celebrated minimax principle of Yao says that for any Boolean-valued function f with finite domain, there is a distribution µ over the domain of f such that computing f to error against inputs from µ is just as hard as computing f to error on worst-case inputs. Notably, however, the distribution µ depends on the target error level1: the hard distribution which is tight for bounded error might be trivial to solve to small bias, and the hard distribution which is tight for a small bias level might be far from tight for bounded error levels. In this work, we introduce a new type of minimax theorem which can provide a hard distribution µ that works for all bias levels at once. We show that this works for randomized query complexity, randomized communication complexity, some randomized circuit models, quantum query and communication complexities, approximate polynomial degree, and approximate logrank. We also prove an improved version of Impagliazzo's hardcore lemma. Our proofs rely on two innovations over the classical approach of using Von Neumann's minimax theorem or linear programming duality. First, we use Sion's minimax theorem to prove a minimax theorem for ratios of bilinear functions representing the cost and score of algorithms. Second, we introduce a new way to analyze low-bias randomized algorithms by viewing them as "forecasting algorithms" evaluated by a certain proper scoring rule. The expected score of the forecasting version of a randomized algorithm appears to be a more fine-grained way of analyzing the bias of the algorithm. We show that such expected scores have many elegant mathematical properties--for example, they can be amplified linearly instead of quadratically. We anticipate forecasting algorithms will find use in future work in which a fine-grained analysis of small-bias algorithms is required. [ABSTRACT FROM AUTHOR]

In public libraries, especially those in rural settings, it is important that every dime of library funding is leveraged effectively into serving the community. As part of a year-long project beginning in January 2023, we are evaluating item-level cost-effectiveness for each circulating item housed at the public library in Lakeville, Indiana. Through the use of big(ish) data, some custom Python scripting, and machine learning algorithms we hope to answer: How much money is saved by library patrons through their use of the public library's physical collection? How much money is saved by the community through the operation of a public library based on the use of the circulating collection? And are there any non-obvious traits which make an item or title a more or less cost-effective circulating asset? In this column, I will describe the scripts, share initial findings, discuss challenges, and investigate next steps. [ABSTRACT FROM AUTHOR]