Restricted Set Research Articles

Practice is the best of all instructors-Effects of enactment encoding and episodic future thinking on prospective memory performance in high-functioning adults with autism spectrum disorder.

Prospective memory (PM) is the ability to remember to carry out intended actions in the future. The present study investigated the effects of episodic future thinking (EFT) and enactment encoding (EE) on PM performance in autistic adults (ASD). A total of 72 autistic individuals and 70 controls matched for age, gender, and cognitive abilities completed a computerized version of the Dresden breakfast Task, which required participants to prepare breakfast following a set of rules and time restrictions. A two (group: ASD vs. controls) by three (encoding condition: EFT vs. EE vs. standard) between-subjects design was applied. Participants were either instructed to engage in EFT or EE to prepare to the different tasks prior to performing the Dresden breakfast or received standard instructions. Analyses of variance were conducted. Autism-spectrum-disorders (ASD) participants did not differ from control participants in their PM performance, regardless of which strategy they used. Compared to the standard condition, EE but not EFT improved time-based PM performance in all participants. This is the first study to find spared time-based PM performance in autistic individuals. The results confirm earlier results of beneficial effects of EE on PM performance. Findings are discussed with regards to the methodology used, sample composition as well as autistic characteristics.

Optimal scheduling of maintenance dredging in a maritime transportation system

A maritime transportation system is a network of ports and commercial terminals connected by navigation channels and navigable inland rivers that enables international trade and the global supply chain. The channels and rivers are subject to recurring sedimentation, which reduces available depths, sailing drafts, and volumes of cargo that vessels can transport between ports. To maintain this network at sufficient depths and enable cost-effective maritime transportation, a specialized fleet of dredging vessels, or dredges, periodically remove accumulated sediment and restore capacity. Scheduling dredges to perform work requires simultaneous consideration of factors specific to the location, dredge, and underlying maritime network and, in practice, often results in significant inefficiencies and delays. Previous models proposed in the literature to optimize dredge scheduling are either intractable or consider only limited aspects of the problem. This paper defines the problem of tactical dredging portfolio scheduling, introduces the General Dredge Scheduling Model (GDSM) as a constraint programming model to solve this problem, and applies GDSM to a realistic problem composed of a portfolio of dredging jobs, fleet of dredges, and sets of seasonal and environmental restrictions.

Sparse subspace clustering in diverse multiplex network model

The paper considers the DIverse MultiPLEx (DIMPLE) network model, where all layers of the network have the same collection of nodes and are equipped with the Stochastic Block Models. In addition, all layers can be partitioned into groups with the same community structures, although the layers in the same group may have different matrices of block connection probabilities. To the best of our knowledge, the DIMPLE model, introduced in Pensky and Wang (2021), presents the most broad SBM-equipped binary multilayer network model on the same set of nodes and, thus, generalizes a multitude of papers that study more restrictive settings. Under the DIMPLE model, the main task is to identify the groups of layers with the same community structures since the matrices of block connection probabilities act as nuisance parameters under the DIMPLE paradigm. The main contribution of the paper is achieving the strongly consistent between-layer clustering by using Sparse Subspace Clustering (SSC), the well-developed technique in computer vision. In addition, SSC allows to handle much larger networks than spectral clustering, and is perfectly suitable for application of parallel computing. Moreover, our paper is the first one to obtain precision guarantees for SSC when it is applied to binary data.

Teaching Artificial Intelligence Good Air Traffic Flow Management

Air traffic flow managers are continually faced with the decision of when and how to respond to predictions of future constraints. The promise of artificial intelligence, and specifically reinforcement learning, to provide decision support in this domain stems from the ability to systematically evaluate a sequence of potential actions, or strategy, across a range of uncertain futures. As decision support for human traffic managers, the generated recommendations must embody characteristics of a good management strategy; doing so requires introducing such notions to the algorithm. This paper proposes inducing stability into the strategy by dynamically constraining the design space based on upstream design decisions to promote consistency in the recommendations over time, where two such constraint sets are considered. The paper further evaluates the impact of adding a performance improvement threshold that must be overcome to accept a new strategy recommendation. The combination of search constraints and threshold values is evaluated against the agent’s reward function in addition to measures proposed to capture the stability of the strategy. The results show that the more restrictive set of constraints yields the best performance in terms of strategy stability and is more likely to reduce the delay where implementation of the threshold has a minor impact on overall performance. However, for the highest impact day of 8 June 2018, applying the threshold reverses the performance gains in delay but dramatically improves the stability of the resulting traffic flow management strategy from a flight level perspective, implying a potential tradeoff between delay optimization and flight predictability.

Comprehensive investigation on fabrication, spectral characterization and biological importance of Co(II) and Ni(II) heteroleptic complexes

Using the microwave irradiation approach, novel metallic complexes of Cobalt [Co(II)] and Nickel [Ni(II)] complexes of 2-aminothiazole (ATZ) and thiocyanate ligands [IUPAC Name: Di(2-aminothiazole) thiocyanato cobalt(II), Di(2-aminothiazole) thiocyanato nickel(II)] were created. The molecular formulas and the geometry of the complexes have been deduced by elemental analysis, molar conductance, magnetic moment, UV–visible, FT-IR, thermogravimetric analysis, cyclic voltammetry, and powder-XRD techniques. Molar conductance values indicate that the complexes are non-electrolyte (1:0) type. The complexes have octahedral structure is revealed by the magnetic moment and UV–visible spectra. The FT-IR spectra reveal that the 2-aminothiazole and thiocyanate ions are coordinated to the metal ion in a monodentate manner. The antibacterial capabilities of ligands and their Co(II) and Ni(II) compounds were studied against a restricted set of microorganisms utilizing the agar-well diffusion method and varying concentrations.When compared to free 2-aminothiazole ligand, the complexes exhibit possible activity against the fungus than bacteria. By assessing how well the complexes and ligands interact with the stable free radical DPPH, their ability to scavenge free radicals has been established. Comparing the complexes to the free ligand, the complexes exhibit greater antioxidant activity due to the fact that metal complexes are widely recognized for their capacity to accelerate medication action and boost therapeutic agent potency when they coordinate with a metal ion. The primary goal of this research is to develop multimodal medicinal drugs, which include studying the roles of 2-aminothiazole and its transition metal complexes.

Few-shot defect classification via feature aggregation based on graph neural network

The effectiveness of deep learning models is greatly dependent on the availability of a vast amount of labeled data. However, in the realm of surface defect classification, acquiring and annotating defect samples proves to be quite challenging. Consequently, accurately predicting defect types with only a limited number of labeled samples has emerged as a prominent research focus in recent years. Few-shot learning, which leverages a restricted sample set in the support set, can effectively predict the categories of unlabeled samples in the query set. This approach is particularly well-suited for defect classification scenarios. In this article, we propose a transductive few-shot surface defect classification method, which using both the instance-level relations and distribution-level relations in each few-shot learning task. Furthermore, we calculate class center features in transductive manner and incorporate them into the feature aggregation operation to rectify the positioning of edge samples in the mapping space. This adjustment aims to minimize the distance between samples of the same category, thereby mitigating the influence of unlabeled samples at category boundary on classification accuracy. Experimental results on the public dataset show the outstanding performance of our proposed approach compared to the state-of-the-art methods in the few-shot learning settings. Our code is available at https://github.com/Harry10459/CIDnet.

Prevalence and risk factors for long COVID among adults in Scotland using electronic health records: a national, retrospective, observational cohort study

Long COVID is a debilitating multisystem condition. The objective of this study was to estimate the prevalence of long COVID in the adult population of Scotland, and to identify risk factors associated with its development. In this national, retrospective, observational cohort study, we analysed electronic health records (EHRs) for all adults (≥18 years) registered with a general medical practice and resident in Scotland between March 1, 2020, and October 26, 2022 (98-99% of the population). We linked data from primary care, secondary care, laboratory testing and prescribing. Four outcome measures were used to identify long COVID: clinical codes, free text in primary care records, free text on sick notes, and a novel operational definition. The operational definition was developed using Poisson regression to identify clinical encounters indicative of long COVID from a sample of negative and positive COVID-19 cases matched on time-varying propensity to test positive for SARS-CoV-2. Possible risk factors for long COVID were identified by stratifying descriptive statistics by long COVID status. Of 4,676,390 participants, 81,219 (1.7%) were identified as having long COVID. Clinical codes identified the fewest cases (n=1,092, 0.02%), followed by free text (n=8,368, 0.2%), sick notes (n=14,469, 0.3%), and the operational definition (n=64,193, 1.4%). There was limited overlap in cases identified by the measures; however, temporal trends and patient characteristics were consistent across measures. Compared with the general population, a higher proportion of people with long COVID were female (65.1% versus 50.4%), aged 38-67 (63.7% versus 48.9%), overweight or obese (45.7% versus 29.4%), had one or more comorbidities (52.7% versus 36.0%), were immunosuppressed (6.9% versus 3.2%), shielding (7.9% versus 3.4%), or hospitalised within 28 days of testing positive (8.8% versus 3.3%%), and had tested positive before Omicron became the dominant variant (44.9% versus 35.9%). The operational definition identified long COVID cases with combinations of clinical encounters (from four symptoms, six investigation types, and seven management strategies) recorded in EHRs within 4-26 weeks of a positive SARS-CoV-2 test. These combinations were significantly (p<0.0001) more prevalent in positive COVID-19 patients than in matched negative controls. In a case-crossover analysis, 16.4% of those identified by the operational definition had similar healthcare patterns recorded before testing positive. The prevalence of long COVID presenting in general practice was estimated to be 0.02-1.7%, depending on the measure used. Due to challenges in diagnosing long COVID and inconsistent recording of information in EHRs, the true prevalence of long COVID is likely to be higher. The operational definition provided a novel approach but relied on a restricted set of symptoms and may misclassify individuals with pre-existing health conditions. Further research is needed to refine and validate this approach. Chief Scientist Office (Scotland), Medical Research Council, and BREATHE.

Joint EURADOS-EANM initiative for an advanced computational framework for the assessment of external dose rates from nuclear medicine patients

Abstract Background In order to ensure adequate radiation protection of critical groups such as staff, caregivers and the general public coming into proximity of nuclear medicine (NM) patients, it is necessary to consider the impact of the radiation emitted by the patients during their stay at the hospital or after leaving the hospital. Current risk assessments are based on ambient dose rate measurements in a single position at a specified distance from the patient and carried out at several time points after administration of the radiopharmaceutical to estimate the whole-body retention. The limitations of such an approach are addressed in this study by developing and validating a more advanced computational dosimetry approach using Monte Carlo (MC) simulations in combination with flexible and realistic computational phantoms and time activity distribution curves from reference biokinetic models. Results Measurements of the ambient dose rate equivalent Ḣ*(10) at 1 m from the NM patient have been successfully compared against MC simulations with 5 different codes using the ICRP adult reference computational voxel phantoms, for typical clinical procedures with 99mTc-HDP/MDP, 18FDG and Na131I. All measurement data fall in the 95% confidence intervals, determined for the average simulated results. Moreover, the different MC codes (MCNP-X, PHITS, GATE, GEANT4, TRIPOLI-4®) have been compared for a more realistic scenario where the effective dose rate Ė of an exposed individual was determined in positions facing and aside the patient model at 30 cm, 50 cm and 100 cm. The variation between codes was lower than 8% for all the radiopharmaceuticals at 1 m, and varied from 5 to 16% for the face-to face and side-by-side configuration at 30 cm and 50 cm. A sensitivity study on the influence of patient model morphology demonstrated that the relative standard deviation of Ḣ*(10) at 1 m for the range of included patient models remained under 16% for time points up to 120 min post administration. Conclusions The validated computational approach will be further used for the evaluation of effective dose rates per unit administered activity for a variety of close-contact configurations and a range of radiopharmaceuticals as part of risk assessment studies. Together with the choice of appropriate dose constraints this would facilitate the setting of release criteria and patient restrictions.

Cosmology in Lorentzian Regge calculus: causality violations, massless scalar field and discrete dynamics

We develop a model of spatially flat, homogeneous and isotropic cosmology in Lorentzian Regge calculus, employing four-dimensional Lorentzian frusta as building blocks. By examining the causal structure of the discrete spacetimes obtained by gluing such four-frusta in spatial and temporal direction, we find causality violations if the sub-cells connecting spatial slices are spacelike. A Wick rotation to the Euclidean theory can be defined globally by a complexification of the variables and an analytic continuation of the action. Introducing a discrete free massless scalar field, we study its equations of motion and show that it evolves monotonically. Furthermore, in a continuum limit, we obtain the equations of a homogeneous scalar field on a spatially flat Friedmann background. Vacuum solutions to the causally regular Regge equations are static and flat and show a restoration of time reparametrisation invariance. In the presence of a scalar field, the height of a frustum is a dynamical variable that has a solution if causality violations are absent and if an inequality relating geometric and matter boundary data is satisfied. Edge lengths of cubes evolve monotonically, yielding a contracting or an expanding branch of the Universe. In a small deficit angle expansion, the system can be deparametrised via the scalar field and a continuum limit of the discrete theory can be defined which we show to yield the relational Friedmann equation. These properties are obstructed if higher orders of the deficit angle are taken into account. Our results suggest that the inclusion of timelike sub-cells is necessary for a causally regular classical evolution in this symmetry restricted setting. Ultimately, this works serves as a basis for forthcoming investigations on the cosmological path integral within the framework of effective spin foams.

Specific features of a scaffolding antibody light chain.

The antigen-binding sites in conventional antibodies are formed by hypervariable complementarity-determining regions (CDRs) from both heavy chains (HCs) and light chains (LCs). A deviation from this paradigm is found in a subset of bovine antibodies that bind antigens via an ultra-long CDR. The HCs bearing ultra-long CDRs pair with a restricted set of highly conserved LCs that convey stability to the antibody. Despite the importance of these LCs, their specific features remained unknown. Here, we show that the conserved bovine LC found in antibodies with ultra-long CDRs exhibits a distinct combination of favorable physicochemical properties such as good secretion from mammalian cells, strong dimerization, high stability, and resistance to aggregation. These physicochemical traits of the LCs arise from a combination of the specific sequences in the germline CDRs and a lambda LC framework. In addition to understanding the molecular architecture of antibodies with ultra-long CDRs, our findings reveal fundamental insights into LC characteristics that can guide the design of antibodies with improved properties.

Fasting upregulates the monocarboxylate transporter MCT1 at the rat blood-brain barrier through PPAR δ activation

BackgroundThe blood-brain barrier (BBB) is pivotal for the maintenance of brain homeostasis and it strictly regulates the cerebral transport of a wide range of endogenous compounds and drugs. While fasting is increasingly recognized as a potential therapeutic intervention in neurology and psychiatry, its impact upon the BBB has not been studied. This study was designed to assess the global impact of fasting upon the repertoire of BBB transporters.MethodsWe used a combination of in vivo and in vitro experiments to assess the response of the brain endothelium in male rats that were fed ad libitum or fasted for one to three days. Brain endothelial cells were acutely purified and transcriptionaly profiled using RNA-Seq. Isolated brain microvessels were used to assess the protein expression of selected BBB transporters through western blot. The molecular mechanisms involved in the adaptation to fasting were investigated in primary cultured rat brain endothelial cells. MCT1 activity was probed by in situ brain perfusion.ResultsFasting did not change the expression of the main drug efflux ATP-binding cassette transporters or P-glycoprotein activity at the BBB but modulated a restrictive set of solute carrier transporters. These included the ketone bodies transporter MCT1, which is pivotal for the brain adaptation to fasting. Our findings in vivo suggested that PPAR δ, a major lipid sensor, was selectively activated in brain endothelial cells in response to fasting. This was confirmed in vitro where pharmacological agonists and free fatty acids selectively activated PPAR δ, resulting in the upregulation of MCT1 expression. Moreover, dosing rats with a specific PPAR δ antagonist blocked the upregulation of MCT1 expression and activity induced by fasting.ConclusionsAltogether, our study shows that fasting affects a selected set of BBB transporters which does not include the main drug efflux transporters. Moreover, we describe a previously unknown selective adaptive response of the brain vasculature to fasting which involves PPAR δ and is responsible for the up-regulation of MCT1 expression and activity. Our study opens new perspectives for the metabolic manipulation of the BBB in the healthy or diseased brain.

ISSUES OF CREATING A SYSTEM FOR EARLY DETECTION OF FOREST FIRES BASED ON UNMANNED AERIAL VEHICLES

The urgent importance of the task of monitoring the state of forests lies in the danger of forest fires. Forest fires are a frequent occurrence in almost all continents. Despite the high information content of the spectral signs of a forest fire, the corresponding functionality is not fully used in the known implementations of the forest fire early detection system. The spectral region in which it is possible to distinguish forest areas in a state of fire from their usual state is quite wide and covers the middle and thermal zone of the infrared part of the spectrum. The installation of spectral equipment (multi- and hyperspectrometers) on board the UAV (unmanned aerial vehicle) can certainly expand the functionality of unmanned equipment in the early detection of forest fires. The optimal procedure for using UAVs equipped with hyperspectral equipment for early detection of forest fires is proposed. An optimization problem has been formulated and solved to determine the most appropriate mode of changing the UAV flight altitude with an increase in the wavelength of the measurements being carried out. According to the proposed method, on the right branch of the Planck curve, with increasing wavelength, the flight altitude should be increased, and on the left branch it should be reduced. This order corresponds to the case of increasing the wavelength on both branches and follows from the integral restriction set on the function of the dependence of the flight altitude on the wavelength, which comes from the limited real energy resources on board the UAV. Based on the developed method, a new technique for early detection of forest fires based on the use of hyperspectral method is proposed.

Learning Coalition Structures with Games

Coalitions naturally exist in many real-world systems involving multiple decision makers such as ridesharing, security, and online ad auctions, but the coalition structure among the agents is often unknown. We propose and study an important yet previously overseen problem -- Coalition Structure Learning (CSL), where we aim to carefully design a series of games for the agents and infer the underlying coalition structure by observing their interactions in those games. We establish a lower bound on the sample complexity -- defined as the number of games needed to learn the structure -- of any algorithms for CSL and propose the Iterative Grouping (IG) algorithm for designing normal-form games to achieve the lower bound. We show that IG can be extended to other succinct games such as congestion games and graphical games. Moreover, we solve CSL in a more restrictive and practical setting: auctions. We show a variant of IG to solve CSL in the auction setting even if we cannot design the bidder valuations. Finally, we conduct experiments to evaluate IG in the auction setting and the results align with our theoretical analysis.

Maximizing Nash Social Welfare under Two-Sided Preferences

The maximum Nash social welfare (NSW)---which maximizes the geometric mean of agents' utilities---is a fundamental solution concept with remarkable fairness and efficiency guarantees. The computational aspects of NSW have been extensively studied for *one-sided* preferences where a set of agents have preferences over a set of resources. Our work deviates from this trend and studies NSW maximization for *two-sided* preferences, wherein a set of workers and firms, each having a cardinal valuation function, are matched with each other. We provide a systematic study of the computational complexity of maximizing NSW for many-to-one matchings under two-sided preferences. Our main negative result is that maximizing NSW is NP-hard even in a highly restricted setting where each firm has capacity 2, all valuations are in the range {0,1,2}, and each agent positively values at most three other agents. In search of positive results, we develop approximation algorithms as well as parameterized algorithms in terms of natural parameters such as the number of workers, the number of firms, and the firms' capacities. We also provide algorithms for restricted domains such as symmetric binary valuations and bounded degree instances.

Abstract 6126: Understanding disparities in lung cancer using single cell RNA sequencing data transformed by the Gerchberg Saxton algorithm

Abstract Purpose An overarching goal of this study is to reduce disparities in lung cancer research commonly manifested by racial and/or gender under-representations. By employing the Gerchberg-Saxton (GS) algorithm, we aim to diminish data noise/bias in single-cell RNA sequencing (scRNA-seq) gene expression data, thereby enabling more equitable research outcomes. An immediate goal is to remove extensive data bias associated with scRNA-seq data thus enhancing downstream Machine Learning analyses. Background: We have successfully applied the GS algorithm for fairer mortality rate predictions across different racial groups [1]. In this study, we seek to apply the GS algorithm to the scRNA-seq data that interrogate the cellular landscapes of lung cancer and the tumor microenvironment. Methodology: The application of the GS algorithm to single cell RNA data involved a series of steps. Initially, all data frames were transposed, switching columns and rows to represent gene expressions and single cells, respectively. The algorithm was then meticulously applied to each column, allowing for the uniform distribution of certain specific gene expression information across all single cells within the data frame. This column-wise application was crucial to maintain the integrity of individual cell characteristics while ensuring a fair structural distribution of gene expression data. The process aimed to balance the representation of genes across all cells, addressing inherent biases and noises in the original data structure. Data: The study utilizes a previously unpublished scRNA-seq dataset comprising 14 lung cancer patients from Wake Forest Baptist Comprehensive Cancer Center, including 6 African American and 8 Caucasian patients. Results: One out of 14 preliminary clustering results are presented in Figure 2 using ScanPy [2]. The initial findings are based on a restricted set of cell marker genes, which will be further developed with additional markers in subsequent analyses. With the application of the Gerchberg-Saxton algorithm, the Shannon Entropy [3] analysis revealed a more uniform randomness across gene expressions (Figure 1), and the unsupervised clustering indicated a clearer separation of cell types, significantly enhancing the ability for downstream analyses. The data transformation will be applied to the whole dataset for comparative analyses of cellular landscapes between lung cancer from different races and genders, to be reported at the AACR 2024 Annual Meeting. Conclusions: Our preliminary studies showed that the Gerchberg-Saxton algorithm is effectiveness in normalizing data distribution for scRNA-seq data, which has led to enhanced resolution of cell type differentiations in clustering analysis. With this refined methodology, we are better poised to better address lung cancer health disparities revealed by single cell sequencing analysis. Citation Format: Seha Ay, Liang Liu, Elizabeth Forbes, Umit Topaloglu, Wei Zhang. Understanding disparities in lung cancer using single cell RNA sequencing data transformed by the Gerchberg Saxton algorithm [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6126.

Classical shadows based on locally-entangled measurements

We study classical shadows protocols based on randomized measurements in n-qubit entangled bases, generalizing the random Pauli measurement protocol (n=1). We show that entangled measurements (n&amp;#x2265;2) enable nontrivial and potentially advantageous trade-offs in the sample complexity of learning Pauli expectation values. This is sharply illustrated by shadows based on two-qubit Bell measurements: the scaling of sample complexity with Pauli weight k improves quadratically (from &amp;#x223C;3k down to &amp;#x223C;3k/2) for many operators, while others become impossible to learn. Tuning the amount of entanglement in the measurement bases defines a family of protocols that interpolate between Pauli and Bell shadows, retaining some of the benefits of both. For large n, we show that randomized measurements in n-qubit GHZ bases further improve the best scaling to &amp;#x223C;(3/2)k, albeit on an increasingly restricted set of operators. Despite their simplicity and lower hardware requirements, these protocols can match or outperform recently-introduced "shallow shadows" in some practically-relevant Pauli estimation tasks.

Investigating the impact of 1 hour of daily outdoor access on the gait and hoof health of nonclinically lame cows housed in a movement-restricted environment

Tiestalls, known for restrictive housing, can reduce cows' locomotor skills over time. While outdoor access benefits clinically lame cows, its effect on nonclinically lame cows is less known. This study evaluated 1 h daily outdoor access on gait and hoof health of nonclinically lame Holstein cows in tiestalls. Thirty cows, blocked by parity and DIM, were assigned to exercise (1 h outdoor access 5 d/wk for 5 wk) or nonexercise groups. Visual scoring assessed 6 gait attributes and overall gait (on scales of 0–5 and 1–5, respectively) at pre-trial, post-trial, and 8-wk follow-up stages. A total of 15 cows (9 exercise, 6 nonexercise) underwent visual gait scoring, with logistical challenges and exclusion criteria leading to this selection. Hoof health for all 30 cows was evaluated during pre-trial and follow-up hoof trims, documenting claw lesions. Hoof thermography captured dorsal coronary band views in wk 1 and 5. No significant gait score changes were observed, but exercise cows showed a 1-point improvement in overall gait score and 3 gait attributes after 5 wk of outdoor access, which persisted at follow-up. Sole hemorrhages were the only claw lesions observed, and their prevalence and severity remained consistent between pre-trial and follow-up for both groups. Thermography showed consistent coronary band temperature metrics across groups and over time. In conclusion, brief outdoor sessions resulted in noticeable, albeit not statistically significant, improvements in the gait of nonclinically lame cows in restrictive housing settings without adverse hoof health effects. Further studies should evaluate different outdoor access benefits and use precise gait and hoof health analysis technologies for a more accurate detection of subtle changes.

Controlled bidirectional remote implementations of partially unknown quantum operations

By blending the ideas of controlled teleportation, bidirectional teleportation and remote implementation of quantum operations, we investigate the bidirectional transmission of quantum operations under the control of one supervisor. Firstly, we provide a method for generating a class of multi-particle entangled states, including five-particle and seven-particle entangled states, using a nine-particle entangled state as an example. Then using our constructed five-particle entangled state as a quantum channel, we propose a new controlled bidirectional protocol for remotely implementing partially known quantum operations (PUQOs for short) of one qubit within the two restricted sets satisfying specific conditions. Subsequently, exploiting a nine-particle entangled state as the quantum channel, we extend it to the case of two-qubit quantum operations which come from eight restricted sets. Replacing two PUQO of two qubits by a PUQO of one qubit and a PUQO of two qubits, we put forward another new asymmetric controlled bidirectional protocol for remotely implementing PKQOs via a seven-particle entangled state as the quantum channel. In these protocols, by using universal recovered operations performed by the receivers, the senders can simultaneously exchange their PUQOs as long as the controller collaborates, where each PUQO acts on the corresponding unknown state owned by the remote recipient.

Near universal property of a quadratic form of Hessian discriminant 4

A quadratic form is universal if it represents all totally positive integers in a number field. In classical number theory over the rational integers there are some results where a quadratic form takes on all positive integer values except for a certain restricted set. Here we show that one of the quaternary quadratic forms proven universal for ℚ(√5) takes on all totally positive values in ℚ(√2) except for a certain infinite set. The exceptional integers have even norm and the highest power of 2 dividing that norm is 2 to the first power.

Filtering dynamical systems using observations of statistics.

We consider the problem of filtering dynamical systems, possibly stochastic, using observations of statistics. Thus, the computational task is to estimate a time-evolving density ρ(v,t) given noisy observations of the true density ρ†; this contrasts with the standard filtering problem based on observations of the state v. The task is naturally formulated as an infinite-dimensional filtering problem in the space of densities ρ. However, for the purposes of tractability, we seek algorithms in state space; specifically, we introduce a mean-field state-space model, and using interacting particle system approximations to this model, we propose an ensemble method. We refer to the resulting methodology as the ensemble Fokker-Planck filter (EnFPF). Under certain restrictive assumptions, we show that the EnFPF approximates the Kalman-Bucy filter for the Fokker-Planck equation, which is the exact solution to the infinite-dimensional filtering problem. Furthermore, our numerical experiments show that the methodology is useful beyond this restrictive setting. Specifically, the experiments show that the EnFPF is able to correct ensemble statistics, to accelerate convergence to the invariant density for autonomous systems, and to accelerate convergence to time-dependent invariant densities for non-autonomous systems. We discuss possible applications of the EnFPF to climate ensembles and to turbulence modeling.