Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable Deterministic Integration of Two Quantum Dots into an On-Chip Quantum Circuit

Integrated quantum photonic circuits (IQPCs) with deterministically integrated quantum emitters are critical elements for scalable quantum information applications and have attracted significant attention in recent years. However, scaling up them towards fully functional photonic circuits with multiple deterministically integrated quantum emitters to generate photonic input states remains a great challenge. In this work, we report on a monolithic prototype IQPC consisting of two pre-selected quantum dots deterministically integrated into nanobeam cavities at the input ports of a 2x2 multimode interference beam-splitter. The on-chip beam splitter exhibits a splitting ratio of nearly 50/50 and the integrated quantum emitters have high single-photon purity, enabling on-chip HBT experiments, depicting deterministic scalability. Overall, this marks a cornerstone toward scalable and fully-functional IQPCs.
QPanda: high-performance quantum computing framework for multiple application scenarios

With the birth of Noisy Intermediate Scale Quantum (NISQ) devices and the verification of"quantum supremacy"in random number sampling and boson sampling, more and more fields hope to use quantum computers to solve specific problems, such as aerodynamic design, route allocation, financial option prediction, quantum chemical simulation to find new materials, and the challenge of quantum cryptography to automotive industry security. However, these fields still need to constantly explore quantum algorithms that adapt to the current NISQ machine, so a quantum programming framework that can face multi-scenarios and application needs is required. Therefore, this paper proposes QPanda, an application scenario-oriented quantum programming framework with high-performance simulation. Such as designing quantum chemical simulation algorithms based on it to explore new materials, building a quantum machine learning framework to serve finance, etc. This framework implements high-performance simulation of quantum circuits, a configuration of the fusion processing backend of quantum computers and supercomputers, and compilation and optimization methods of quantum programs for NISQ machines. Finally, the experiment shows that quantum jobs can be executed with high fidelity on the quantum processor using quantum circuit compile and optimized interface and have better simulation performance.
A Class-wise Non-salient Region Generalized Framework for Video Semantic Segmentation

—Video semantic segmentation (VSS) is beneﬁcial for dealing with dynamic scenes due to the continuous property of the real-world environment. On the one hand, some methods alleviate the predicted inconsistent problem between continuous frames. On the other hand, other methods employ the previous frame as the prior information to assist in segmenting the current frame. Although the previous methods achieve superior performances on the independent and identically distributed (i.i.d) data, they can not generalize well on other unseen domains. Thus, we explore a new task, the video generalizable semantic segmentation (VGSS) task that considers both continuous frames and domain generalization. In this paper, we propose a class-wise non-salient region generalized (CNSG) framework for the VGSS task. Concretely, we ﬁrst deﬁne the class-wise non-salient feature, which describes features of the class-wise non-salient region that carry more generalizable information. Then, we propose a class-wise non-salient feature reasoning strategy to select and enhance the most generalized channels adaptively. Finally, we propose an inter-frame non-salient centroid alignment loss to alleviate the predicted inconsistent problem in the VGSS task. We also extend our video-based framework to the image-based generalizable semantic segmentation (IGSS) task. Experiments demonstrate that our CNSG framework yields signiﬁcant improvement in the VGSS and IGSS tasks.
Modeling unsteady loads on wind-turbine blade sections from periodic structural oscillations and impinging gusts

Many traditional methods for wind turbine design and analysis assume quasi-steady aerodynamics, but atmospheric flows are inherently unsteady and modern turbine blades are susceptible to aeroelastic deformations. This study therefore evaluates the effectiveness of simple analytical models for capturing the effects of such unsteady conditions on wind-turbine blades. We consider a pitching and plunging airfoil in a periodic transverse gust as an idealization of unsteady loading scenarios on a blade section. A potential-flow model derived from a linear combination of canonical problems is proposed to predict the unsteady lift on a two-dimensional airfoil in the small-perturbation limit. We then perform high-fidelity two-dimensional numerical simulations of a NACA-0012 airfoil over a range of periodic pitch, plunge, and gust disturbances, and quantify the amplitude and phase of the unsteady lift response. Good agreement with the model predictions is found for low to moderate forcing amplitudes and frequencies, while deviations are observed when the angle-of-attack amplitudes approach the static flow-separation limit of the airfoil. Potential explanations are given for the cases in which the ideal-flow theory proves insufficient. This theoretical framework and numerical evaluation motivate the inclusion of unsteady flow models in design and simulation tools in order to increase the robustness and operational lifespans of wind turbine blades in real flow conditions.
An Entropy-Based Model for Hierarchical Learning

Machine learning is the dominant approach to artificial intelligence, through which computers learn from data and experience. In the framework of supervised learning, a necessity for a computer to learn from data accurately and efficiently is to be provided with auxiliary information about the data distribution and target function through the learning model. This notion of auxiliary information relates to the concept of regularization in statistical learning theory. A common feature among real-world datasets is that data domains are multiscale and target functions are well-behaved and smooth. This paper proposes an entropy-based learning model that exploits this data structure and discusses its statistical and computational benefits. The hierarchical learning model is inspired by human beings' logical and progressive easy-to-hard learning mechanism and has interpretable levels. The model apportions computational resources according to the complexity of data instances and target functions. This property can have multiple benefits, including higher inference speed and computational savings in training a model for many users or when training is interrupted. We provide a statistical analysis of the learning mechanism using multiscale entropies and show that it can yield significantly stronger guarantees than uniform convergence bounds.
PCCC: The Pairwise-Confidence-Constraints-Clustering Algorithm

We consider a semi-supervised k-clustering problem where information is available on whether pairs of objects are in the same or in different clusters. This information is either available with certainty or with a limited level of confidence. We introduce the PCCC algorithm, which iteratively assigns objects to clusters while accounting for the information provided on the pairs of objects. Our algorithm can include relationships as hard constraints that are guaranteed to be satisfied or as soft constraints that can be violated subject to a penalty. This flexibility distinguishes our algorithm from the state-of-the-art in which all pairwise constraints are either considered hard, or all are considered soft. Unlike existing algorithms, our algorithm scales to large-scale instances with up to 60,000 objects, 100 clusters, and millions of cannot-link constraints (which are the most challenging constraints to incorporate). We compare the PCCC algorithm with state-of-the-art approaches in an extensive computational study. Even though the PCCC algorithm is more general than the state-of-the-art approaches in its applicability, it outperforms the state-of-theart approaches on instances with all hard constraints or all soft constraints both in terms of running time and various metrics of solution quality. The source code of the PCCC algorithm is publicly available on GitHub.
Biologically Plausible Learning on Neuromorphic Hardware Architectures

Movement of model parameters from memory to computing elements in deep learning (DL) has led to a growing imbalance known as the memory wall. Neuromorphic computation-in-memory (CIM) is an emerging paradigm that addresses this imbalance by performing computations directly in analog memory. However, sequential backpropagation of error through a network in DL prevents efficient parallelization. A novel method, direct feedback alignment (DFA), resolves layer dependencies by directly passing the error from the output to each layer. This work explores the interrelationship of implementing a bio-plausible learning algorithm like DFA in-situ on neuromorphic CIM hardware, emphasizing energy, area, and latency constraints. Using the DNN+NeuroSim benchmarking framework, we investigate the impact of hardware nonidealities and quantization on algorithm performance, as well as how network topologies and algorithm-level design choices scale latency, energy, and area consumption of a chip. While standard backpropagation learns more accurate models when faced with hardware imperfections, DFA enables significant speedup through parallelization, reducing training time by a factor approaching N for N-layer networks.
DNA Unzipping as PT-Symmetry Breaking Transition

We show that classical DNA unzipping transition which is equivalently described by quantum mechanical localization-delocalization transition in the ground state of non-Hermitian Hatano-Nelson Hamiltonian is underpinned by generalized parity (P)-time reversal (T) symmetry breaking transition. We also study the one-dimensional discretized version of Hatano-Nelson model in the presence of the single impurity and quasiperiodic potential on a finite-size lattice. Our results show that these models undergo a phase transition from a PT unbroken phase to a broken phase. Interestingly, the generalized PT phase transition points also coincide with the localization-delocalization transition for both models.
ON THE GENERATION OF BIPARTITE GRIDS IN 2 AND 3 DIMENSIONAL REGIONS

We present a procedure to generate bipartite grids for simply connected domains in 2-D and 3-D of prescribed size and controlled regularity elements. The mesh elements K of the triangulation satisfy ζK ≤ C where ζK is the regularity and C is a constant depending on the shape parameters of the initial mesh. Bipartite grids permit a well-posed mixed-mixed variational formulation of problems such as the porous media flow equation and other linear physical phenomena as well as Galerkin-type discontinuous relaxations.
Unbiasedness as the Dual of Being Bayes

In this note we define the notion of unbiasedness for a decision function for an arbitrary loss function. This is a generalization of Lehmann's (1951) definition. We show that this notion of unbiasedness is a dual to the notion of being Bayes; that is, if the role of the random variable and the parameter is interchanged, then unbiasedness is equivalent to being Bayes and vice versa. Some consequences of this fact are discussed.
Layer Imbalance Aware Multiplex Network Embedding

Multiplex network embedding is an effective technique to jointly learn the low-dimensional representations of nodes across network layers. However, the number of edges among layers may vary significantly. This data imbalance will lead to performance degradation especially on the sparse layer due to learning bias and the adverse effects of irrelevant or conflicting data in other layers. In this paper, a Layer Imbalance Aware Multiplex Network Embedding (LIAMNE) method is proposed where the edges in auxiliary layers are under-sampled based on the node similarity in the embedding space of the target layer to achieve balanced edge distribution and to minimize noisy relations that are less relevant to the target layer. Real-world datasets with different degrees of layer imbalance are used for experimentation. The results demonstrate that LIAMNE significantly outperforms several state-of-the-art multiplex network embedding methods in link prediction on the target layer. Meantime, the comprehensive representation of the entire multiplex network is not compromised by the sampling method as evaluated by its performance on the node classification task.
Robust Bayesian Subspace Identification for Small Data Sets

Model estimates obtained from traditional subspace identification methods may be subject to significant variance. This elevated variance is aggravated in the cases of large models or of a limited sample size. Common solutions to reduce the effect of variance are regularized estimators, shrinkage estimators and Bayesian estimation. In the current work we investigate the latter two solutions, which have not yet been applied to subspace identification. Our experimental results show that our proposed estimators may reduce the estimation risk up to $40\%$ of that of traditional subspace methods.
Asynchronous Hybrid Reinforcement Learning for Latency and Reliability Optimization in the Metaverse Over Wireless Communications

Technology advancements in wireless communications and high-performance Extended Reality (XR) have empowered the developments of the Metaverse. The demand for the Metaverse applications and hence, real-time digital twinning of real-world scenes is increasing. Nevertheless, the replication of 2D physical world images into 3D virtual objects is computationally intensive and requires computation offloading. The disparity in transmitted object dimension (2D as opposed to 3D) leads to asymmetric data sizes in uplink (UL) and downlink (DL). To ensure the reliability and low latency of the system, we consider an asynchronous joint UL-DL scenario where in the UL stage, the smaller data size of the physical world images captured by multiple extended reality users (XUs) will be uploaded to the Metaverse Console (MC) to be construed and rendered. In the DL stage, the larger-size 3D virtual objects need to be transmitted back to the XUs. We design a novel multi-agent reinforcement learning algorithm structure, namely Asynchronous Actors Hybrid Critic (AAHC), to optimize the decisions pertaining to computation offloading and channel assignment in the UL stage and optimize the DL transmission power in the DL stage. Extensive experiments demonstrate that compared to proposed baselines, AAHC obtains better solutions with satisfactory training time.
A note on commutative semigroups

From Theorem 19.1 of [1] it follows that the lattice of varieties (equational classes) of Abelian groups is distributive. We show that the lattice of varieties of commutative semigroups is not modular by considering an example from the dual lattice of closed commutative semigroup laws. We will assume and use without comment that any given set of laws contains the commutative and associative laws. The binary operation is denoted by juxtaposition. By (a, b), where a and b are positive integers, we will mean the term xayb. Let the sets A', B', and C' of laws be given by the following table.
Standardizing Representation for Equality with a Population Seat Index

Proportional representation (PR) has long been believed the ideal system for the equality of individuals in apportioning the seats of a legislature body to subgroups. We observe that PR implicitly assumes the (standard) number of representatives is proportional to the population, a situation no longer observed since 1820s. To address this issue, we suggest to formulate the apportionment problem in a broader context by explicitly specifying a standard function $f$ such that $f(p)$ is the standard, possibly fractional number of representatives for population $p$, where PR assumes $f(p)\propto p$. For this generalized apportionment problem, we give a population seat index (PSI) $\frac{f^{-1}(s)}{p}$ for quantifying the contribution of an individual in assigning $s$ seats to a population $p$, where $f^{-1}$ is the inverse of $f$. With the PSI, we derive apportioning schemes with absolute and relative individual equality. Particularly, for $s$ seats, populations $p_1, \ldots, p_k$, and a standard function $f(p) = a + b p^\gamma$ with constants $a, b, \gamma \ge 0$, the ideal, possibly fractional number of seats for subgroup $i$ is $a + \frac{(S-ka)p_i^{\gamma}}{\sum p_j^{\gamma}}$, not $\frac{Sp_i}{\sum p_j}$ calculated by PR which works only for $a=0$, $\gamma=1$. Finally, since real-world observations indicate a standard function $f \propto p^\gamma$ with $\gamma<1$, we conclude that PR represents individuals in less populous subgroups less than individuals in more populous subgroups.
A New Subspace Iteration Algorithm for Solving Generalized Eigenvalue Problems

It is needed to solve generalized eigenvalue problems (GEP) in many applications, such as the numerical simulation of vibration analysis, quantum mechanics, electronic structure, etc. The subspace iteration is a kind of widely used algorithm to solve eigenvalue problems. To solve the generalized eigenvalue problem, one kind of subspace iteration method, Chebyshev-Davidson algorithm, is proposed recently. In Chebyshev-Davidson algorithm, the Chebyshev polynomial filter technique is incorporated in the subspace iteration. In this paper, based on Chebyshev-Davidson algorithm, a new subspace iteration algorithm is constructed. In the new algorithm, the Chebyshev filter and inexact Rayleigh quotient iteration techniques are combined together to enlarge the subspace in the iteration. Numerical results of a vibration analysis problem show that the number of iteration and computing time of the proposed algorithm is much less than that of the Chebyshev-Davidson algorithm and some typical GEP solution algorithms. Furthermore, the new algorithm is more stable and reliable than the Chebyshev-Davidson algorithm in the numerical results.
Beryllium gaskets suitable for pressures up to 10 GPa

A systematic study of the possibility of using beryllium gaskets in diamond‐anvil pressure cells was carried out. Six different Be‐BeO alloys were tested and treated in different ways in order to obtain Be gaskets suitable for pressures up to 10 GPa. The beryllium quality, the shape of the diamond anvils, the thickness of the gasket, the diameter of the sample chamber, and the depth of the gasket preindentation determine the highest obtainable pressure and its stability.
Examining Political Rhetoric with Epistemic Stance Detection

Participants in political discourse employ rhetorical strategies—such as hedging, attributions, or denials—to display varying degrees of belief commitments to claims proposed by themselves or others. Traditionally, political scientists have studied these epistemic phenomena through labor-intensive manual content analysis. We propose to help automate such work through epistemic stance prediction, drawn from research in computational semantics, to distinguish at the clausal level what is asserted, denied, or only ambivalently suggested by the author or other mentioned entities (belief holders). We first develop a simple RoBERTa-based model for multi-source stance predictions that outperforms more complex state-of-the-art modeling. Then we demonstrate its novel application to political science by conducting a large-scale analysis of the Mass Market Manifestos corpus of U.S. political opinion books, where we characterize trends in cited belief holders—respected allies and opposed bogeymen—across U.S. political ideologies.
Properties of Group Fairness Metrics for Rankings

In recent years, several metrics have been developed for evaluating group fairness of rankings. Given that these metrics were developed with different application contexts and ranking algorithms in mind, it is not straightforward which metric to choose for a given scenario. In this paper, we perform a comprehensive comparative analysis of existing group fairness metrics developed in the context of fair ranking. By virtue of their diverse application contexts, we argue that such a comparative analysis is not straightforward. Hence, we take an axiomatic approach whereby we design a set of thirteen properties for group fairness metrics that consider different ranking settings. A metric can then be selected depending on whether it satisfies all or a subset of these properties. We apply these properties on eleven existing group fairness metrics, and through both empirical and theoretical results we demonstrate that most of these metrics only satisfy a small subset of the proposed properties. These findings highlight limitations of existing metrics, and provide insights into how to evaluate and interpret different fairness metrics in practical deployment. The proposed properties can also assist practitioners in selecting appropriate metrics for evaluating fairness in a specific application.
Gauge theory description of Rydberg atom arrays with a tunable blockade radius

We discuss a Rydberg atom chain with a tunable blockade radius from the gauge theoretic perspective. When the blockade radius is one lattice spacing, this system can be formulated in terms of the PXP model, and there is a $\mathbb{Z}_2$ Ising phase transition known to be equivalent to a confinement-deconfinement transition in a gauge theory, the lattice Schwinger model. Further increasing the blockade radius, one can add a next-nearest neighbor (NNN) interaction into the PXP model. We discuss the interpretation of NNN interaction in terms of the gauge theory and how finite NNN interaction alters the deconfinement behavior and propose a corresponding experimental protocol. When the blockade radius reaches two lattice spacing, the model reduces to the PPXPP model. A novel gauge theory equivalent to the PPXPP model is formulated, and the phases in the two formulations are delineated. These results are readily explored experimentally in Rydberg quantum simulators.
Wireless Semantic Communication: A Networking Perspective

—Semantic communication (SemCom) has recently been considered a promising solution to guarantee high resource utilization and transmission reliability for future wireless networks. Nevertheless, the unique demand for background knowledge matching makes it challenging to achieve efficient wireless resource management for multiple users in SemCom-enabled networks (SC-Nets). To this end, this paper investigates SemCom from a networking perspective, where two fundamental problems of user association (UA) and bandwidth allocation (BA) are systematically addressed in the SC-Net. First, considering varying knowledge matching states between mobile users and associated base stations, we identify two general SC-Net scenarios, namely perfect knowledge matching-based SC-Net and imperfect knowledge matching-based SC-Net. Afterward, for each SC-Net scenario, we describe its distinctive semantic channel model from the semantic information theory perspective, whereby a concept of bit-rate-to-message-rate transformation is developed along with a new semantics-level metric, namely system throughput in message (STM), to measure the overall network performance. In this way, we then formulate a joint STM-maximization problem of UA and BA for each SC-Net scenario, followed by a corresponding optimal solution proposed. Numerical results in both scenarios demonstrate significant superiority and reliability of our solutions in the STM performance compared with two benchmarks.