CATP: Context-Aware Trajectory Prediction with Competition Symbiosis

Contextual information is vital for accurate trajectory prediction. For instance, the intricate flying behavior of migratory birds hinges on their analysis of environmental cues such as wind direction and air pressure. However, the diverse and dynamic nature of contextual information renders it an arduous task for AI models to comprehend its impact on trajectories and consequently predict them accurately. To address this issue, we propose a ``manager-worker'' framework to unleash the full potential of contextual information and construct CATP model, an implementation of the framework for Context-Aware Trajectory Prediction. The framework comprises a manager model, several worker models, and a tailored training mechanism inspired by competition symbiosis in nature. Taking CATP as an example, each worker needs to compete against others for training data and develop an advantage in predicting specific moving patterns. The manager learns the workers' performance in different contexts and selects the best one in the given context to predict trajectories, enabling CATP as a whole to operate in a symbiotic manner. We conducted two comparative experiments and an ablation study to quantitatively evaluate the proposed framework and CATP model. The results showed that CATP could outperform SOTA models, and the framework could be generalized to different context-aware tasks

Highly Efficient Server-Aided Multiparty Subfield VOLE Distribution Protocol

In recent development of secure multi-party computation (MPC), pseudorandom correlations of subfield vector oblivious linear evaluation (sVOLE) type become popular due to their amazing applicability in multi-dimensional MPC protocols such as privacy-preserving biometric identification and privacy-preserving machine learning protocols. In this paper, we introduce a novel way of VOLE distribution in three-party and four-party honest majority settings with the aid of a trusted server. This new method significantly decreases the communication cost and the memory storage of random sVOLE instances. On the other hand, it also enables a streamline distribution process that can generate a sVOLE instance of an arbitrary length, which results in 100 percent of utility rate of random sVOLE in multi-dimensional MPC protocols while preserving complete precomputability

The Stable Limit DAHA and the Double Dyck Path Algebra

The double Dyck path algebra (DDPA) is the key algebraic structure that governs the phenomena behind the shuffle and rational shuffle conjectures. The structure emerged from their considerations and computational experiments while attacking the conjecture. Nevertheless, the DDPA bears some resemblance to the structure of a type A double affine Hecke algebra (DAHA). While trying to address this resemblance, Carlsson and Mellit noted one aspect that differentiates the two structures and speculated on how they could be ultimately related. The goal of my thesis is to explain how the DDPA emerges naturally and canonically (as a stable limit) from the family of GLn DAHA's

Entangled X-ray Photon Pair Generation by Free Electron Lasers

Einstein, Podolsky and Rosen's prediction on incompleteness of quantum mechanics was overturned by experimental tests on Bell's inequality that confirmed the existence of quantum entanglement. In X-ray optics, entangled photon pairs can be generated by X-ray parametric down conversion (XPDC), which is limited by relatively low efficiency. Meanwhile, free electron laser (FEL) has successfully lased at X-ray frequencies recently. However, FEL is usually seen as a classical light source, and its quantum effects are considered minor corrections to the classical theory. Here we investigate entangled X-ray photon pair emissions in FEL. We establish a theory for coherently amplified entangled photon pair emission from microbunched electron pulses in the undulator. We also propose an experimental scheme for the observation of the entangled photon pairs via energy and spatial correlation measurements. Such an entangled X-ray photon pair source is of great importance in quantum optics and other X-ray applications.Comment: 13 pages, 3 figure

Metal-like behavior of a 2D molecular catalyst enables redox-decoupled electrocatalysis

Molecular catalysts facilitate electrochemical conversion by changing their oxidation states to transfer electrons. However, this redox-mediated mechanism features stepwise electron transfer and substrate activation in separate elementary steps, thereby resulting in an inherent loss in efficiency. Here, we synthesize a two-dimensional (2D) iron phthalocyanine (FePc) material and uncover its non-mediated electron transfer behavior in electrocatalysis, which overcomes the conventional redox-mediated limitation in the oxygen reduction reaction (ORR) pathway that molecular catalysts face. The 2D geometry enables the FePc molecules to be positioned within the electrochemical double layer, enabling electrons to directly transfer to oxygen reactants, prior to the Fe(II/III) redox. This functions in a manner akin to a metal catalyst thereby opening a redox-decoupled ORR mechanism. As a result, the reported 2D FePc molecular catalyst exhibits unprecedented ORR half-wave potential at 0.945 V vs. the reversible hydrogen electrode, achieving efficient application in zinc-air batteries and H2/O2 fuel cells. These findings open new possibilities in voltage efficient, redox-decoupled molecular catalysis that integrates strengths of molecules and materials in one synergistic system.</p

Bibliometric analysis of electroencephalogram research in mild cognitive impairment from 2005 to 2022

BackgroundElectroencephalogram (EEG), one of the most commonly used non-invasive neurophysiological examination techniques, advanced rapidly between 2005 and 2022, particularly when it was used for the diagnosis and prognosis of mild cognitive impairment (MCI). This study used a bibliometric approach to synthesize the knowledge structure and cutting-edge hotspots of EEG application in the MCI.MethodsRelated publications in the Web of Science Core Collection (WosCC) were retrieved from inception to 30 September 2022. CiteSpace, VOSviewer, and HistCite software were employed to perform bibliographic and visualization analyses.ResultsBetween 2005 and 2022, 2,905 studies related to the application of EEG in MCI were investigated. The United States had the highest number of publications and was at the top of the list of international collaborations. In terms of total number of articles, IRCCS San Raffaele Pisana ranked first among institutions. The Clinical Neurophysiology published the greatest number of articles. The author with the highest citations was Babiloni C. In descending order of frequency, keywords with the highest frequency were “EEG,” “mild cognitive impairment,” and “Alzheimer’s disease”.ConclusionThe application of EEG in MCI was investigated using bibliographic analysis. The research emphasis has shifted from examining local brain lesions with EEG to neural network mechanisms. The paradigm of big data and intelligent analysis is becoming more relevant in EEG analytical methods. The use of EEG to link MCI to other related neurological disorders, and to evaluate new targets for diagnosis and treatment, has become a new research trend. The above-mentioned findings have implications in the future research on the application of EEG in MCI

Efficient Secure Multiparty Computation for Multidimensional Arithmetics and Its Application in Privacy-Preserving Biometric Identification

Over years of the development of secure multi-party computation (MPC), many sophisticated functionalities have been made pratical and multi-dimensional operations occur more and more frequently in MPC protocols, especially in protocols involving datasets of vector elements, such as privacy-preserving biometric identification and privacy-preserving machine learning. In this paper, we introduce a new kind of correlation, called tensor triples, which is designed to make multi-dimensional MPC protocols more efficient. We will discuss the generation process, the usage, as well as the applications of tensor triples and show that it can accelerate privacy-preserving biometric identification protocols, such as FingerCode, Eigenfaces and FaceNet, by more than 1000 times

Unprotected quadratic band crossing points and quantum anomalous Hall effect in FeB2 monolayer

Quadratic band crossing points (QBCPs) and quantum anomalous Hall effect (QAHE) have attracted the attention of both theoretical and experimental researchers in recent years. Based on first-principle calculations, we find that the FeB$_2$ monolayer is a nonmagnetic semimetal with QBCPs at $K$. Through symmetry analysis and $\mathbf{k}\cdot\mathbf{p}$ invariant theory, we find that the QBCP is not protected by rotation symmetry and consists of two Dirac points with same chirality (Berry phase of $2\pi$). Once introducing Coulomb interactions, we find that there is a spontaneous-time-reversal-breaking instability of the spinful QBCPs, which gives rise to a $C=2$ QAH insulator with orbital moment ordering

Spatio-temporal characteristics of PM\u3csub\u3e2.5\u3c/sub\u3e, PM\u3csub\u3e10\u3c/sub\u3e, and AOD over the central line project of China’s South-NorthWater diversion in Henan Province (China)

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. The spatio-temporal characteristics of particulate matter with a particle size less than or equal to 2.5 µm (PM2.5), particulate matter with a particle size less than or equal to 10 µm (PM10), meteorological parameters from September 2018 to September 2019, and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) aerosol optical depth (AOD) from 2007 to 2019 were investigated over the Central Line Project of China’s South-North Water Diversion (CSNWD) in Henan Province. To better understand the characteristics of the atmospheric environment over the CSNWD, air quality monitoring stations were installed in Nanyang (in the upper reaches), Zhengzhou (in the middle reaches), and Anyang (in the lower reaches). In this study, daily, monthly, and seasonal statistical analyses of PM2.5 and PM10 concentrations were performed and their relationship with meteorological parameters was investigated. The results show extremely poor air quality conditions over the Zhengzhou Station compared with the Nanyang and Anyang Stations. The annual average PM2.5 concentration did not meet China’s ambient air secondary standard (35 µg/m3 annual mean) over all the stations, while the annual average PM10 concentration satisfied China’s ambient air secondary standard (100 µg/m3 annual mean) over the Anyang and Nanyang Stations, except for the Zhengzhou Station. The highest PM2.5 and PM10 concentrations were observed during winter compared with the other seasons. The results show that PM2.5 and PM10 concentrations were negatively correlated with wind speed and temperature at the Nanyang and Zhengzhou Stations, but positively correlated with relative humidity. However, no significant negative or positive correlation was observed at Anyang Station. There is a strong linear positive correlation between PM2.5 and PM10 (R = 0.99), which indicates that the particulate matter at the three stations was mainly caused by local emissions. Additionally, the AOD values at the three stations were the highest in summer, which may be related to the residues of crops burned in Henan Province in summer