Cell-free radio access networks (CF-RANs) with network-assisted free-duplex (NA-FD) architecture unify flexible-duplex, hybrid-duplex, and full-duplex operations, but the coupled uplink/downlink interference and centralized processing impose substantial computational and signaling burdens. To address these challenges, this paper studies distributed transceiver design and access point (AP) duplex mode selection under edge distributed unit (EDU)-level information constraints and per- AP power limitations. We develop a partial distributed block coordinate descent (PDBCD) algorithm that decomposes the original sum-rate maximization into three tractable subproblems and iteratively approximates MMSE performance. The proposed design fully leverages EDU computing resources, reducing the computation load at the cloud computing unit (CCU), and significantly lowering fronthaul signaling overhead through an adaptive inter-EDU information sharing mechanism. Furthermore, by integrating a low-complexity greedy search for duplex mode assignment, the algorithm effectively mitigates cross-link interference (CLI) in NA-FD systems. Simulation results show that the proposed scheme improves spectral efficiency compared with conventional duplex mode, and centralized/distributed MMSE baselines, while maintaining strong scalability under practical deployment constraints.

Terahertz (THz) communication is emerging as a pivotal enabler for 6G and beyond wireless systems owing to its multi-GHz bandwidth. One of its novel applications is in wireless data centers, where it enables ultra-high data rates while enhancing network reconfigurability and scalability. However, due to numerous racks, supporting walls, and densely deployed antennas, the line-of-sight (LoS) path in data centers is often instead of fully obstructed, resulting in quasi-LoS propagation and degradation of spectral efficiency. To address this issue, Airy beam-based hybrid beamforming is investigated in this paper as a promising technique to mitigate quasi-LoS propagation and enhance spectral efficiency in THz wireless data centers. Specifically, a cascaded geometrical and wave channel model (CGWCM) is proposed for quasi-LoS scenarios, which accounts for diffraction effects while being more simplified than conventional wave-based model. Then, the characteristics and generation of the Airy beam are analyzed, and beam search methods for quasi-LoS scenarios are proposed, including hierarchical focusing-Airy beam search, and low-complexity beam search. Simulation results validate the effectiveness of the CGWCM and demonstrate the superiority of the Airy beam over Gaussian beams in mitigating blockages, verifying its potential for practical THz wireless communication in data centers.

Terahertz (THz) communication is envisioned as a key technology for 6G and beyond wireless systems owing to its multi-GHz bandwidth. To maintain the same aperture area and the same link budget as the lower frequencies, ultra-massive multi-input and multi-output (UM-MIMO) with hybrid beamforming is promising. Nevertheless, the hardware imperfections particularly at THz frequencies, can degrade spectral efficiency and lead to a high symbol error rate (SER), which is often overlooked yet imperative to address in practical THz communication systems. In this paper, the hybrid beamforming is investigated for THz UM-MIMO systems accounting for comprehensive hardware imperfections, including DAC and ADC quantization errors, in-phase and quadrature imbalance (IQ imbalance), phase noise, amplitude and phase error of imperfect phase shifters and power amplifier (PA) nonlinearity. Then, a two-stage hardware imperfection compensation algorithm is proposed. In the first stage, a deep neural network (DNN) based unified hardware imperfection model is developed to represent the combined hardware imperfections. Furthermore, to balance the performance and model complexity, a tailored network slimming framework is proposed using three slimming methods including pruning, parameter sharing, and power-aware scheme to slim the network in the first stage. In the second stage, the digital precoder in the transmitter (Tx) or the combiner in the receiver (Rx) is designed using neural network (NN) to effectively compensate for these imperfections. Numerical results show that the Tx compensation can perform better than the Rx compensation. Additionally, using the combined slimming methods can reduce parameters by 97.2% and running time by 39.2% while maintaining nearly the same performance in both uncoded and coded systems.

Handwritten Mathematical Expression Recognition (HMER) has extensive applications in automated grading and office automation. However, existing sequence-based decoding methods, which directly predict LaTeX sequences, struggle to understand and model the inherent tree structure of LaTeX and often fail to ensure syntactic correctness in the decoded results. To address these challenges, we propose a novel model named TAMER (Tree-Aware Transformer) for handwritten mathematical expression recognition. TAMER introduces an innovative Tree-aware Module while maintaining the flexibility and efficient training of Transformer. TAMER combines the advantages of both sequence decoding and tree decoding models by jointly optimizing sequence prediction and tree structure prediction tasks, which enhances the model's understanding and generalization of complex mathematical expression structures. During inference, TAMER employs a Tree Structure Prediction Scoring Mechanism to improve the structural validity of the generated LaTeX sequences. Experimental results on CROHME datasets demonstrate that TAMER outperforms traditional sequence decoding and tree decoding models, especially in handling complex mathematical structures, achieving state-of-the-art (SOTA) performance.

This paper investigates the downlink performance of cell-free radio access networks (CF-RAN) employing distributed regularized zero-forcing (RZF) precoding under imperfect channel state information (CSI). We derive a deterministic equivalent expression for the spectral efficiency (SE) lower bound under large-system regime. The proposed bound accurately approximates the system performance while requiring only statistical channel information, formulated based on the principles of large dimensional random matrix theory (RMT). Simulation results demonstrate that the deterministic approximation achieves high accuracy across various configurations, especially in scenarios with a limited number of edge distribution units (EDUs) per user. The derived lower bound provides a tractable and efficient analytical tool for evaluating CF-RAN performance in extreme MIMO (E-MIMO) scenarios, and offers practical guidance for system design, resource allocation, and 6G network planning.

Terahertz (THz) communication is regarded as a key technology for 6G and beyond wireless systems due to its broad continuous bandwidth. However, the THz band suffers from a considerable propagation loss which limits the communication distance. Thanks to the submillimeter wavelength, ultra-massive multi-input multi-output (UM-MIMO) beamforming is able to enhance the received power and overcome the distance limitation. Nevertheless, hardware imperfections especially at THz frequencies, can lead to significant beamforming gain loss and degrade spectral efficiency, which are imperative to address in practical communication systems. In this paper, a user-by-user block-diagonalization (UBU-BD) algorithm in dynamic array-of-subarrays (DAoSA) architecture for multi-user UM-MIMO systems is firstly proposed under the ideal hardware assumption. Then, power losses as results of four kinds of critical hardware imperfectness, including digital-to-analog converter (DAC), analog-to-digital converter (ADC), phase shifter and switch, are modeled and evaluated. To overcome the spectral efficiency loss caused by hardware imperfectness, a switch-power algorithm is developed and assessed. Simulation results show that combinations of extra switches and transmit power are helpful to fully compensate for the spectral efficiency losses.

In this paper, we propose an advanced variational model for image segmentation and bias correction. In contrast to the majority of existing level set segmentation models that only consider illumination bias fields, we additionally consider the impact of image reflectance on segmentation accuracy. Our method is capable of effectively segmenting images with blurry edge structures affected by non-uniform illumination. In order to enhance segmentation efficiency, we directly segment the underlying structures of the images, construct spatial prior and apply adaptive regularization constraints on the structural component. Therefore, in the process of segmentation, the proposed algorithm can accurately identify object boundaries without being affected by the environment. Besides, the GL operator is applied to enhance the robustness of the model against noise. Furthermore, we use the alternating direction method of multipliers and the operator splitting algorithm for numerical solution. The experimental results obtained from various sorts of images illustrate that our model outperforms many leading-edge level set models with regard to robustness, corrected results and accuracy.

Terahertz (THz) communication technology is considered as one of the key technologies of sixth-generation (6G) communications. The study of THz channel characteristics is the basis for evaluating, deploying, and optimizing THz communication systems. In this paper, we conduct measurements in line-of-sight (LOS) and non-LOS (NLOS) scenarios in an indoor-office scenario at 100 GHz. Based on measured data, path loss (PL) and root-mean-squared (RMS) delay spread (DS) are analyzed and modeled. First, the differences between omnidirectional and directional PL are analyzed. And by analyzing the distance dependence of RMS DS, it is found that RMS DS in the NLOS scenario is negatively proportional to propagation distance while the trend is opposite in the LOS scenario. Second, based on the statistical modeling method, the model parameters of PL and RMS DS are obtained. We compare the obtained model to the 3GPP channel model and analyze their differences. Third, the bit-error-rate (BER) performance of OFDM systems at 100 GHz is simulated based on measured power delay profiles (PDPs). We find that the scenario and propagation distance have an apparent influence on the BER performance. These results can give insight into the design of THz communication systems in the indoor-office scenario.

Counterfactual explanations help users to understand the behaviors of machine learning models by changing the inputs for the existing outputs. For an image classification task, an example counterfactual visual explanation explains: "for an example that belongs to class A, what changes do we need to make to the input so that the output is more inclined to class B." Our research considers changing the attribute description text of class A on the basis of the attributes of class B and generating counterfactual images on the basis of the modified text. We can use the prediction results of the model on counterfactual images to find the attributes that have the greatest effect when the model is predicting classes A and B. We applied our method to a fine-grained image classification dataset and used the generative adversarial network to generate natural counterfactual visual explanations. To evaluate these explanations, we used them to assist crowdsourcing workers in an image classification task. We found that, within a specific range, they improved classification accuracy.

The Ethereum ecosystem has introduced a pervasive blockchain platform with programmable transactions. Everyone is allowed to develop and deploy smart contracts. Such flexibility can lead to a large collection of similar contracts, i.e., clones, especially when Ethereum applications are highly domain-specific and may share similar functionalities within the same domain, e.g., token contracts often provide interfaces for money transfer and balance inquiry. While smart contract clones have a wide range of impact across different applications, e.g., security, they are relatively little studied. Although clone detection has been a long-standing research topic, blockchain smart contracts introduce new challenges, e.g., syntactic diversity due to trade-off between storage and execution, understanding high-level business logic etc.. In this paper, we highlighted the very first attempt to clone detection of Ethereum smart contracts. To overcome the new challenges, we introduce the concept of smart contract birthmark, i.e., a semantic-preserving and computable representation for smart contract bytecode. The birthmark captures high-level semantics by effectively sketching symbolic execution traces (e.g., data access dependencies, path conditions) and maintain syntactic regularities (e.g., type and number of instructions) as well. Then, the clone detection problem is reduced to a computation of statistical similarity between two contract birthmarks. We have implemented a clone detector called EClone and evaluated it on Ethereum. The empirical results demonstrated the potential of EClone in accurately identifying clones. We have also extended EClone for vulnerability search and managed to detect CVE-2018-10376 instances.

Smart contracts, as a promising and powerful application on the Ethereum blockchain, have been growing rapidly in the past few years. Since they are highly vulnerable to different forms of attacks, their security becomes a top priority. However, existing security auditing techniques are either limited in fnding vulnerabilities (rely on pre-defned bug paterns) or very expensive (rely on program analysis), thus are insufcient for Ethereum.

The Ethereum ecosystem has created a prosperity of smart contract applications in public blockchains, with transparent, traceable and programmable transactions. However, the flexibility that everybody can write and deploy smart contracts on Ethereum causes a large collection of similar contracts, i.e., clones. In practice, smart contract clones may amplify severe threats like security attacks, resource waste etc.

In this paper, we describe a new method to generate a smooth algebraic spline (AS) approximation of the molecular surface (MS) based on an initial coarse triangulation derived from the atomic coordinate information of the biomolecule, resident in the Protein data bank (PDB). Our method first constructs a triangular prism scaffold covering the PDB structure, and then generates a piecewise polynomial F on the Bernstein-Bezier (BB) basis within the scaffold. An ASMS model of the molecular surface is extracted as the zero contours of F, which is nearly C1 and has dual implicit and parametric representations. The dual representations allow us easily do the point sampling on the ASMS model and apply it to the accurate estimation of the integrals involved in the electrostatic solvation energy computations. Meanwhile comparing with the trivial piecewise linear surface model, fewer number of sampling points are needed for the ASMS, which effectively reduces the complexity of the energy estimation.

Electrostatic interactions play a significant role in determining the binding affinity of molecules and drugs. While significant effort has been devoted to the accurate computation of biomolecular electrostatics based on an all-atomic solution of the Poisson-Boltzmann (PB) equation for smaller proteins and nucleic acids, relatively little has been done to optimize the efficiency of electrostatic energetics and force computations of macromolecules at varying resolutions (also called coarse-graining). We have developed an efficient and comprehensive framework for computing coarse-grained PB electrostatic potentials, polarization energetics and forces for smooth multi-resolution representations of almost all molecular structures, available in the PDB. Important aspects of our framework include the use of variational methods for generating C2-smooth and multi-resolution molecular surfaces (as dielectric interfaces), a parameterization and discretization of the PB equation using an algebraic spline boundary element method, and the rapid estimation of the electrostatic energetics and forces using a kernel independent fast multipole method. We present details of our implementation, as well as several performance results on a number of examples.

In this paper, we describe a new method to generate a smooth algebraic spline (AS) model approximation of the molecular surface (MS), based on an initial coarse triangulation derived from the atomic coordinate information of the biomolecule, resident in the PDB (Protein data bank). Our method first constructs a triangular prism scaffold Ps covering the PDB structure, and then generates piecewise polynomial Bernstein-Bezier (BB) spline function approximation F within Ps, which are nearly C1 everywhere. Approximation error and point sampling convergence bounds are also computed. An implicit AS model of the MS which is free of singularity, is extracted as the zero contours of F. Furthermore, we generate a polynomial parametrization of the implicit MS, which allows for an efficient point sampling on the MS, and thereby simplifies the accurate estimation of integrals needed for electrostatic solvation energy calculations.