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Supervised learning often requires a large number of labeled examples, which has become a critical bottleneck in the case that manual annotating the class labels is costly. To mitigate this issue, a new framework called pairwise comparison (Pcomp) classification is proposed to allow training examples only weakly annotated with pairwise comparison, i.e., which one of two examples is more likely to be positive. The previous study solves Pcomp problems by minimizing the classification error, which may lead to less robust model due to its sensitivity to class distribution. In this paper, we propose a robust learning framework for Pcomp data along with a pairwise surrogate loss called Pcomp-AUC. It provides an unbiased estimator to equivalently maximize AUC without accessing the precise class labels. Theoretically, we prove the consistency with respect to AUC and further provide the estimation error bound for the proposed method. Empirical studies on multiple datasets validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Discourse relation classification is a fundamental task for discourse analysis, which is essential for understanding the structure and connection of texts. Implicit discourse relation classification aims to determine the relationship between adjacent sentences and is very challenging because it lacks explicit discourse connectives as linguistic cues and sufficient annotated training data. In this paper, we propose a discriminative instance selection method to construct synthetic implicit discourse relation data from easy-to-collect explicit discourse relations. An expanded instance consists of an argument pair and its sense label. We introduce the argument pair type classification task, which aims to distinguish between implicit and explicit argument pairs and select the explicit argument pairs that are most similar to natural implicit argument pairs for data expansion. We also propose a simple label-smoothing technique to assign robust sense labels for the selected argument pairs. We evaluate our method on PDTB 2.0 and PDTB 3.0. The results show that our method can consistently improve the performance of the baseline model, and achieve competitive results with the state-of-the-art models. [ABSTRACT FROM AUTHOR]

Entity alignment (EA) is an important technique aiming to find the same real entity between two different source knowledge graphs (KGs). Current methods typically learn the embedding of entities for EA from the structure of KGs for EA. Most EA models are designed for rich-resource languages, requiring sufficient resources such as a parallel corpus and pre-trained language models. However, low-resource language KGs have received less attention, and current models demonstrate poor performance on those low-resource KGs. Recently, researchers have fused relation information and attributes for entity representations to enhance the entity alignment performance, but the relation semantics are often ignored. To address these issues, we propose a novel Semantic-aware Graph Neural Network (SGNN) for entity alignment. First, we generate pseudo sentences according to the relation triples and produce representations using pre-trained models. Second, our approach explores semantic information from the connected relations by a graph neural network. Our model captures expanded feature information from KGs. Experimental results using three low-resource languages demonstrate that our proposed SGNN approach out performs better than state-of-the-art alignment methods on three proposed datasets and three public datasets. [ABSTRACT FROM AUTHOR]

Conclusion: In this paper, we propose the first fully dynamic parallel allpairs shortest path algorithm in the MPC model with a worstcase update rounds of O (n 2 3 − α 6 log n / α) . We compare our algorithm with the existing static APSP algorithms in the MPC model, demonstrating the efficiency of our approach [ABSTRACT FROM AUTHOR]

Accurate monitoring of urban waterlogging contributes to the city's normal operation and the safety of residents' daily travel. However, due to feedback delays or high costs, existing methods make large-scale, fine-grained waterlogging monitoring impossible. A common method is to forecast the city's global waterlogging status using its partial waterlogging data. This method has two challenges: first, existing predictive algorithms are either driven by knowledge or data alone; and second, the partial waterlogging data is not collected selectively, resulting in poor predictions. To overcome the aforementioned challenges, this paper proposes a framework for large-scale and fine-grained spatiotemporal waterlogging monitoring based on the opportunistic sensing of limited bus routes. This framework follows the Sparse Crowdsensing and mainly comprises a pair of iterative predictor and selector. The predictor uses the collected waterlogging status and the predicted status of the uncollected area to train the graph convolutional neural network. It combines both knowledge-driven and data-driven approaches and can be used to forecast waterlogging status in all regions for the upcoming term. The selector consists of a two-stage selection procedure that can select valuable bus routes while satisfying budget constraints. The experimental results on real waterlogging and bus routes in Shenzhen show that the proposed framework could easily perform urban waterlogging monitoring with low cost, high accuracy, wide coverage, and fine granularity. [ABSTRACT FROM AUTHOR]

Deterministic databases are able to reduce coordination costs in a replication. This property has fostered a significant interest in the design of efficient deterministic concurrency control protocols. However, the state-of-the-art deterministic concurrency control protocol Aria has three issues. First, it is impractical to configure a suitable batch size when the read-write set is unknown. Second, Aria running in low-concurrency scenarios, e.g., a single-thread scenario, suffers from the same conflicts as running in high-concurrency scenarios. Third, the single-version schema brings write-after-write conflicts. To address these issues, we propose Gria, an efficient deterministic concurrency control protocol. Gria has the following properties. First, the batch size of Gria is auto-scaling. Second, Gria's conflict probability in low-concurrency scenarios is lower than that in high-concurrency scenarios. Third, Gria has no write-after-write conflicts by adopting a multi-version structure. To further reduce conflicts, we propose two optimizations: a reordering mechanism as well as a rechecking strategy. The evaluation result on two popular benchmarks shows that Gria outperforms Aria by 13x. [ABSTRACT FROM AUTHOR]

This paper explores the conditions which make a regular balanced random (k,2s)-CNF formula (1,0)-unsatisfiable with high probability. The conditions also make a random instance of the regular balanced (k − 1,2(k − 1)s)-SAT problem unsatisfiable with high probability, where the instance obeys a distribution which differs from the distribution obeyed by a regular balanced random (k − 1,2(k − 1)s)-CNF formula. Let F be a regular balanced random (k,2s)-CNF formula where k ⩾ 3, then there exists a number s0 such that F is (1,0)-unsatisfiable with high probability if s > s0. A numerical solution of the number s0 when k ∈ {5, 6,..., 14} is given to conduct simulated experiments. The simulated experiments verify the theoretical result. Besides, the experiments also suggest that F is (1,0)-satisfiable with high probability if s is less than a certain value. [ABSTRACT FROM AUTHOR]

Recent years have seen the wide application of natural language processing (NLP) models in crucial areas such as finance, medical treatment, and news media, raising concerns about the model robustness and vulnerabilities. We find that prompt paradigm can probe special robust defects of pre-trained language models. Malicious prompt texts are first constructed for inputs and a pre-trained language model can generate adversarial examples for victim models via maskfilling. Experimental results show that prompt paradigm can efficiently generate more diverse adversarial examples besides synonym substitution. Then, we propose a novel robust training approach based on prompt paradigm which incorporates prompt texts as the alternatives to adversarial examples and enhances robustness under a lightweight minimax-style optimization framework. Experiments on three real-world tasks and two deep neural models show that our approach can significantly improve the robustness of models to resist adversarial attacks. [ABSTRACT FROM AUTHOR]

Nowadays, the personalized recommendation has become a research hotspot for addressing information overload. Despite this, generating effective recommendations from sparse data remains a challenge. Recently, auxiliary information has been widely used to address data sparsity, but most models using auxiliary information are linear and have limited expressiveness. Due to the advantages of feature extraction and no-label requirements, autoencoder-based methods have become quite popular. However, most existing autoencoder-based methods discard the reconstruction of auxiliary information, which poses huge challenges for better representation learning and model scalability. To address these problems, we propose Serial-Autoencoder for Personalized Recommendation (SAPR), which aims to reduce the loss of critical information and enhance the learning of feature representations. Specifically, we first combine the original rating matrix and item attribute features and feed them into the first autoencoder for generating a higher-level representation of the input. Second, we use a second autoencoder to enhance the reconstruction of the data representation of the prediciton rating matrix. The output rating information is used for recommendation prediction. Extensive experiments on the MovieTweetings and MovieLens datasets have verified the effectiveness of SAPR compared to state-of-the-art models. [ABSTRACT FROM AUTHOR]

Many-body localization (MBL) of a disordered interacting boson system in one dimension is studied numerically at the filling faction one-half. The von Neumann entanglement entropy SvN is commonly used to detect the MBL phase transition but remains challenging to be directly measured. Based on the U(1) symmetry from the particle number conservation, SvN can be decomposed into the particle number entropy SN and the configuration entropy SC. In light of the tendency that the eigenstate’s SC nears zero in the localized phase, we introduce a quantity describing the deviation of SN from the ideal thermalization distribution; finite-size scaling analysis illustrates that it shares the same phase transition point with SvN but displays the better critical exponents. This observation hints that the phase transition to MBL might largely be determined by SN and its fluctuations. Notably, the recent experiments [A. Lukin, et al., Science 364, 256 (2019); J. Léonard, et al., Nat. Phys. 19, 481 (2023)] demonstrated that this deviation can potentially be measured through the SN measurement. Furthermore, our investigations reveal that the thermalized states primarily occupy the low-energy section of the spectrum, as indicated by measures of localization length, gap ratio, and energy density distribution. This low-energy spectrum of the Bose model closely resembles the entire spectrum of the Fermi (or spin XXZ) model, accommodating a transition from the thermalized to the localized states. While, owing to the bosonic statistics, the high-energy spectrum of the model allows the formation of distinct clusters of bosons in the random potential background. We analyze the resulting eigenstate properties and briefly summarize the associated dynamics. To distinguish between the phase regions at the low and high energies, a probing quantity based on the structure of SvN is also devised. Our work highlights the importance of symmetry combined with entanglement in the study of MBL. In this regard, for the disordered Heisenberg XXZ chain, the recent pure eigenvalue analyses in [J. Suntajs, et al., Phys. Rev. E 102, 062144 (2020)] would appear inadequate, while methods used in [A. Morningstar, et al., Phys. Rev. B 105, 174205 (2022)] that spoil the U(1) symmetry could be misleading. [ABSTRACT FROM AUTHOR]

Vortex wave and plane wave, as two most fundamental forms of wave propagation, are widely applied in various research fields. However, there is currently a lack of basic mechanism to enable arbitrary conversion between them. In this paper, we propose a new paradigm of extremely anisotropic acoustic metasurface (AM) to achieve the efficient conversion from 2D vortex waves with arbitrary orbital angular momentum (OAM) to plane waves. The underlying physics of this conversion process is ensured by the symmetry shift of AM medium parameters and the directional compensation of phase. Moreover, this novel phenomenon is further verified by analytical calculations, numerical demonstrations, and acoustic experiments, and the deflection angle and direction of the converted plane waves are qualitatively and quantitatively confirmed by a simple formula. Our work provides new possibilities for arbitrary manipulation of acoustic vortex, and holds potential applications in acoustic communication and OAM-based devices. [ABSTRACT FROM AUTHOR]

Developing advanced hydrogen storage materials with high capacity and efficient reversibility is a crucial aspect for utilizing hydrogen source as a promising alternate to fossil fuels. In this paper, we have systematically investigated the hydrogen storage properties of neutral and negatively charged C9N4 monolayer based on density functional theory (DFT). Our foundings indicate that injecting additional electrons into the adsorbent significantly boosts the adsorption capacity of C9N4 monolayer to H2 molecules. The gravimetric density of negatively charged C9N4 monolayer can reach up to 10.80 wt% when fully covered with hydrogen. Unlike other hydrogen storage methods, the storage and release processes happen automatically upon introducing or removing extra electrons. Moreover, these operations can be easily adjusted through activating or deactivating the charging voltage. As a result, the method is easily reversible and has tunable kinetics without requiring particular activators. Significantly, C9N4 is proved to be a suitable candidate for efficient electron injection/release due to its well electrical conductivity. Our work can serve as a valuable guide in the quest for a novel category of materials for hydrogen storage with high capacity. [ABSTRACT FROM AUTHOR]

By accurately measuring composition and energy spectrum of cosmic ray, the origin problem of so called "knee" region (energy >one PeV) can be solved. However, up to the present, the results of the spectrum in the knee region obtained by several previous experiments have shown obvious differences, so they cannot give effective evidence for judging the theoretical models on the origin of the knee. Recently, the Large High Altitude Air Shower Observatory (LHAASO) has reported several major breakthroughs and important results in astro-particle physics field. Relying on its advantages of wide-sky survey, high altitude location and large area detector arrays, the research content of LHAASO experiment mainly includes ultra high-energy gamma-ray astronomy, measurement of cosmic ray spectra in the knee region, searching for dark matter and new phenomena of particle physics at higher energy. The electron and thermal neutron detector (EN-Detector) is a new scintillator detector which applies thermal neutron detection technology to measure cosmic ray extensive air shower (EAS). This technology is an extension of LHAASO. The EN-Detector Array (ENDA) can highly efficiently measure thermal neutrons generated by secondary hadrons so called "skeleton" of EAS. In this paper, we perform the optimization of ENDA configuration, and obtain expectations on the ENDA results, including thermal neutron distribution, trigger efficiency and capability of cosmic ray composition separation. The obtained real data results are consistent with those by the Monte Carlo simulation. [ABSTRACT FROM AUTHOR]

Currently, magnetic storage devices are encountering the problem of achieving lightweight and high integration in mobile computing devices during the information age. As a result, there is a growing urgency for two-dimensional half-metallic materials with a high Curie temperature (TC). This study presents a theoretical investigation of the fundamental electromagnetic properties of the monolayer hexagonal lattice of Mn2X3 (X = S, Se, Te). Additionally, the potential application of Mn2X3 as magneto-resistive components is explored. All three of them fall into the category of ferromagnetic half-metals. In particular, the Monte Carlo simulations indicate that the TC of Mn2S3 reachs 381 K, noticeably greater than room temperature. These findings present notable advantages for the application of Mn2S3 in spintronic devices. Hence, a prominent spin filtering effect is apparent when employing non-equilibrium Green's function simulations to examine the transport parameters. The resulting current magnitude is approximately 2 × 104 nA, while the peak gigantic magnetoresistance exhibits a substantial value of 8.36 × 1016 %. It is noteworthy that the device demonstrates a substantial spin Seebeck effect when the temperature differential between the electrodes is modified. In brief, Mn2X3 exhibits outstanding features as a high TC half-metal, exhibiting exceptional capabilities in electrical and thermal drives spin transport. Therefore, it holds great potential for usage in spintronics applications. [ABSTRACT FROM AUTHOR]

Nonmagnetic impurity scattering is known to shift up the Ginzburg–Landau parameter κ of a superconductor. In this case, when the system is initially in type I, it can change its magnetic response, crossing the intertype domain with κ ∼ 1 between the two standard superconductivity types and arriving at type II. In the present work we demonstrate that the impact of disorder can be much more profound in the presence of the multiband structure of the charge carrier states. In particular, when the band diffusivities differ from each other, the intertype domain tends to expand significantly, including points with κ ≫ 1 that belong to deep type-II in conventional single-band superconductors. Our finding sheds light on the nontrivial disorder effect and significantly complements earlier results on the enlargement of the intertype domain in clean multiband superconductors. [ABSTRACT FROM AUTHOR]

We theoretically studied the exciton geometric structure in layered semiconducting transition metal dichalcogenides. Based on a three-orbital tight-binding model for Bloch electrons which incorporates their geometric structures, an effective exciton Hamiltonian is constructed and solved perturbatively to reveal the relation between the exciton and its electron/hole constituent. We show that the electron—hole Coulomb interaction gives rise to a non-trivial inheritance of the exciton geometric structure from Bloch electrons, which manifests as a valley-dependent center-of-mass anomalous Hall velocity of the exciton when two external fields are applied on the electron and hole constituents, respectively. The obtained center-of-mass anomalous velocity is found to exhibit a non-trivial dependence on the fields, as well as the wave function and valley index of the exciton. These findings can serve as a general guide for the field-control of the valley-dependent exciton transport, enabling the design of novel quantum optoelectronic and valleytronic devices. [ABSTRACT FROM AUTHOR]

We present a novel feedback control method for quantum systems. Feedback does not affect the controlled system itself. Instead, it controls the unravelling of the quantum channel of interaction between the system and its environment. This interaction can be represented as a history of events. If their informational content is changed, their back-action on the system is also modified. Feedback action is trigged by the events, thus granting the system the degree of control over its own state. The efficiency of the proposed scheme is demonstrated on the example of two-mode atomic Bose-Einstein condensate, with one of its modes subject to phase-contrast imaging in a Mach–Zehnder interferometer. The histories of photocounts in the output channels of the interferometer are used for feedback. Its capabilities of state engineering are studied for different settings of the feedback loop and different numbers of events in the recorded histories. [ABSTRACT FROM AUTHOR]

Solving non-Hermitian quantum many-body systems on a quantum computer by minimizing the variational energy is challenging as the energy can be complex. Here, we propose a variational quantum algorithm for solving the non-Hermitian Hamiltonian by minimizing a type of energy variance, where zero variance can naturally determine the eigenvalues and the associated left and right eigenstates. Moreover, the energy is set as a parameter in the cost function and can be tuned to scan the whole spectrum efficiently by using a two-step optimization scheme. Through numerical simulations, we demonstrate the algorithm for preparing the left and right eigenstates, verifying the biorthogonal relations, as well as evaluating the observables. We also investigate the impact of quantum noise on our algorithm and show that its performance can be largely improved using error mitigation techniques. Therefore, our work suggests an avenue for solving non-Hermitian quantum many-body systems with variational quantum algorithms on near-term noisy quantum computers. [ABSTRACT FROM AUTHOR]

The study of macro continuous flow has a long history. Simultaneously, the exploration of heat and mass transfer in small systems with a particle number of several hundred or less has gained significant interest in the fields of statistical physics and nonlinear science. However, due to absence of suitable methods, the understanding of mesoscale behavior situated between the aforementioned two scenarios, which challenges the physical function of traditional continuous fluid theory and exceeds the simulation capability of microscopic molecular dynamics method, remains considerably deficient. This greatly restricts the evaluation of effects of mesoscale behavior and impedes the development of corresponding regulation techniques. To access the mesoscale behaviors, there are two ways: from large to small and from small to large. Given the necessity to interface with the prevailing macroscopic continuous modeling currently used in the mechanical engineering community, our study of mesoscale behavior begins from the side closer to the macroscopic continuum, that is from large to small. Focusing on some fundamental challenges encountered in modeling and analysis of near-continuous flows, we review the research progress of discrete Boltzmann method (DBM). The ideas and schemes of DBM in coarse-grained modeling and complex physical field analysis are introduced. The relationships, particularly the differences, between DBM and traditional fluid modeling as well as other kinetic methods are discussed. After verification and validation of the method, some applied researches including the development of various physical functions associated with discrete and non-equilibrium effects are illustrated. Future directions of DBM related studies are indicated. [ABSTRACT FROM AUTHOR]

Laser-induced breakdown spectroscopy (LIBS) is regarded as the future superstar for analytical chemistry and widely applied in various fields. Improving the quality of LIBS signal is fundamental to achieving accurate quantification and large-scale commercialization of LIBS. To propose control methods that improve LIBS signal quality, it is essential to have a comprehensive understanding of the influence of key parameters, such as ambient gas pressure, temperature, and sample temperature on LIBS signals. To date, extensive research has been carried out. However, different researchers often yield significantly different experimental results for LIBS, preventing the formation of consistent conclusions. This greatly prevents the understanding of influencing laws of key parameters and the improvement of LIBS quantitative performance. Taking ambient gas pressure as an example, this paper compares the effects of ambient gas pressure under different optimization conditions, reveals the influence of spatiotemporal window caused by inherent characteristics of LIBS signal sources, i.e., intense temporal changes and spatial non-uniformity of laser-induced plasmas, on the impact patterns of key parameters. From the perspective of plasma spatiotemporal evolution, the paper elucidates the influence patterns of ambient gas pressure on LIBS signals, clarifying seemingly contradictory research results in the literature. [ABSTRACT FROM AUTHOR]

Whether the complex game system composed of a large number of artificial intelligence (AI) agents empowered with reinforcement learning can produce extremely favorable collective behaviors just through the way of agent self-exploration is a matter of practical importance. In this paper, we address this question by combining the typical theoretical model of resource allocation system, the minority game model, with reinforcement learning. Each individual participating in the game is set to have a certain degree of intelligence based on reinforcement learning algorithm. In particular, we demonstrate that as AI agents gradually becomes familiar with the unknown environment and tries to provide optimal actions to maximize payoff, the whole system continues to approach the optimal state under certain parameter combinations, herding is effectively suppressed by an oscillating collective behavior which is a self-organizing pattern without any external interference. An interesting phenomenon is that a first-order phase transition is revealed based on some numerical results in our multi-agents system with reinforcement learning. In order to further understand the dynamic behavior of agent learning, we define and analyze the conversion path of belief mode, and find that the self-organizing condensation of belief modes appeared for the given trial and error rates in the AI system. Finally, we provide a detection method for period-two oscillation collective pattern emergence based on the Kullback–Leibler divergence and give the parameter position where the period-two appears. [ABSTRACT FROM AUTHOR]

In this paper, we present a version of the Schur inequality in the setting of Murray–von Neumann algebras, extending a result by Arveson and Kadison. We also describe the ring isomorphisms between ∗ -subalgebras of two Murray–von Neumann algebras. A short proof of the commutator estimation theorem for Murray–von Neumann algebras is given as an easy application. [ABSTRACT FROM AUTHOR]

In this paper, we are concerned with the various properties of the Toeplitz operators acting on the Dirichlet spaces. First, we consider the matrix representation of Toeplitz operators with harmonic and monomial symbols. Second, we establish the expansivity and contractivity of the Toeplitz operators T φ with monomial symbols φ . Third, we give a necessary and sufficient conditions for the normality and hyponormality of the Toeplitz operators T φ with such symbols on the Dirichlet spaces. [ABSTRACT FROM AUTHOR]

This paper explores the role of teledermatology (TD) in Mohs micrographic surgery (MMS) at various stages of patient care. The study aims to assess the benefits, limitations, and patient experiences surrounding TD integration into MMS practices. We conducted a PubMed search using keywords related to TD and MMS, categorizing selected articles into pre-operative, intra-operative, and post-operative stages of MMS. TD reduced waiting times (26.10 days for TD compared to 60.57 days for face-to-face [FTF]) and consultation failure rates (6% for TD vs. 17% for FTF) for MMS preoperative consultations. It also shortened time to treatment by two weeks and led to notable travel savings (162.7 min, 144.5 miles, and $60.00 per person). Telepathology facilitated communication and decision-making during MMS, improving accuracy and efficiency, especially in challenging cases requiring collaboration where physical presence of another surgeon or pathologist is not feasible. Telepathology definitively diagnosed benign lesions and malignant tumors in 81.8% of cases (18/22). Additionally, there was a 95% agreement between conventional light microscopy diagnosis and telepathology in tumors (19/20), and 100% agreement for all 20 Mohs frozen section consultations. For post-operative follow-up, telephone follow-up (TFU) and text messaging proved effective, cost-efficient alternatives with high patient satisfaction (94% in New Zealand and 96% in the U.K.) and early complication identification. This study underscores TD's multifaceted benefits in MMS: enhanced patient experience preoperatively, improved communication during surgery, and cost-effective postoperative follow-up. Limitations include the financial expense and technical issues that can arise with TD (connectivity problems, delays in video/audio transmission, etc.). Further studies are needed to explore emerging TD modalities in post-operative patient management. The integration of TD into MMS signifies a progressive step in dermatological care, offering convenient, cost-effective, and better solutions with the potential to enhance patient experiences and outcomes. [ABSTRACT FROM AUTHOR]