Modern circuit design process increasingly adopts high-level hardware construction languages and parameterized design methodologies to shorten development cycles and maintain high reusability, in contrast to traditional hardware description languages. Such designs often involve complex datapath with arithmetic operations, wide bit-vectors, and on-chip memories, whose scale and level of modeling often pose significant challenges to formal datapath verification. Traditional bit-level SAT sweeping techniques lack the necessary abstraction and adaptability that are required to establish equivalence at a higher level. In this paper, we propose FORWORD, a novel word-level sweeping verification engine tailored explicitly to formal datapath verification. FORWORD integrates randomized and constraint-driven word-level simulations, leveraging adaptive optimization to dynamically refine equivalent candidates identified during simulation. Experimental results demonstrate that FORWORD significantly outperforms state-of-the-art bit-level SAT sweeping engines and the monolithic SMT solving method, thanks to its enhanced capability in effectively identifying equivalent pairs. To the best of our knowledge, FORWORD is the first word-level sweeping engine explicitly designed for datapath verification, offering improved efficiency and adaptability to modern circuit designs.

Oriented to low-altitude economy, integrated air-ground-space communication system and ultra-dense mobile communication network access, reconfigurable intelligent surfaces-mounted unmanned aerial vehicle (RIS-UAV) offer a dynamic solution for propagation challenges in millimeter-wave (mmWave) communications. This work aims to present a comprehensive analytical framework for RIS-UAV assisted mmWave communications in multiple scenarios, encompassing single-base station single-user terminal (SBSU), single-base station multi-user terminal (SBMU), multi-base station single-user terminal (MBSU), and multi-base station multi-user terminal (MBMU) scenarios. We first establish path-loss models and flexible beam management considering UAV translational and rotational posture movements separately within the SBSU scenario. For multi-user scenarios, we propose a space division multiple access (SDMA) over RIS scheme leveraging RIS element partitioning. For multi-base station scenarios, a mobile RIS-UAV phase coordination (MRUPC) strategy is proposed, utilizing the controlled mobility of the UAV to simplify phase compensation requirements for signals from multiple base stations. Extensive numerical simulations validate the accuracy of the derived path-loss model and the feasibility of the proposed beam management scheme for each scenario. The results demonstrate that SDMA over RIS effectively achieves multi-user mmWave beamforming. In addition, the proposed MRUPC strategy exhibits substantial performance gains (approx. 6-8 dB) over the static scheme in the MBSU scenario, validating the feasibility and superiority of leveraging UAV mobility for multi-base station coordination. This study comprehensively provides a systematic theoretical foundation and physical-layer solutions for the design and flexible deployment of joint RIS-UAV mmWave systems in complex wireless access scenarios.

Chinese poetry, a pinnacle of cultural expression, encapsulates human emotions and societal narratives in succinct, evocative language. Its unique blend of linguistic constraints and musicality makes analyzing its pronunciation's emotional features crucial for enriching children's linguistic prowess and artistic appreciation. To this end, we propose CPFformer, a novel deep learning framework, merges phonetics, sentiment analysis in order to analyze and predict the emotional feature of Chinese poetry pronunciation effectively. CPFformer, which consists of anomaly detection in spatial network (ADSN), spatial-temporal learning (STL), dimension segmentation and embedding (DSW), extraction of temporal attention (ETA), and encoder-decoder module (EDSM) modules, employs graph structures to capture the global and local consistency of emotional features across spatial and temporal, and a multiscale Mel feature extraction technique ensures comprehensive analysis of speech dynamics, enhancing emotional expression understanding. The mean square error (mse), mean absolute error (MAE), residual standard error (RSE), and $R$ -square ( $R^{2}$ ) of experiments reach 0.3572, 0.2486, 0.2014, and 0.9822, respectively, demonstrating its feasibility and effectiveness, exhibiting its superiority to the state-of-the-art approaches. The creation of a dedicated Chinese poetry pronunciation dataset marks a significant contribution, facilitating further research. The potential of CPFformer in speech technology and education heralds a new era, fostering the integration of traditional culture and artificial intelligence, and promoting the advancement of emotional literacy and smart learning environments. Its interdisciplinary implications promise exciting avenues for research and application.

Currently, e-commerce platforms integrate ads and organic content into a mixed list for users. While platforms seek to maximize profit from advertisers, organic items enhance user experience. To ensure long-term development, platforms aim to design mechanisms that optimize both revenue and user satisfaction. Current methods rank ads and organic items separately before integrating them. Even if each part is locally optimal, the combined result may not be globally optimal. In this paper, we come up with the Joint Integrated Regret Network (JINTER Net). Unlike traditional methods, which pre-order ads and organic items separately, JINTER Net directly selects from the combined set of candidate ads and organic items to generate an optimal list. This approach aims to optimally balance platform revenue and user experience while satisfying approximate dominant strategy incentive compatibility and individual rationality. We validate the effectiveness of JINTER Net using both synthetic data and real dataset, and our experimental results show that it significantly outperforms baseline models across multiple metrics.

Accurate attribution for multiple platforms is critical for evaluating performance-based advertising. However, existing attribution methods rely heavily on the heuristic methods, e.g., Last-Click Mechanism (LCM) which always allocates the attribution to the platform with the latest report, lacking theoretical guarantees for attribution accuracy. In this work, we propose a novel theoretical model for the advertising attribution problem, in which we aim to design the optimal dominant strategy incentive compatible (DSIC) mechanisms and evaluate their performance. We first show that LCM is not DSIC and performs poorly in terms of accuracy and fairness. To address this limitation, we introduce the Peer-Validated Mechanism (PVM), a DSIC mechanism in which a platform's attribution depends solely on the reports of other platforms. We then examine the accuracy of PVM across both homogeneous and heterogeneous settings, and provide provable accuracy bounds for each case. Notably, we show that PVM is the optimal DSIC mechanism in the homogeneous setting. Finally, numerical experiments are conducted to show that PVM consistently outperforms LCM in terms of attribution accuracy and fairness.

Recently, many e-commerce platforms have favored presenting a mixed list of ads and organic content to users. The widely-used approach separately ranks ads and organic items, then sequentially inserts ads into the list of organic items. However, this method yields sub-optimal results. Firstly, it only ensures that each generated ad and organic item list achieves local optimality, while the predetermined insertion order fails to guarantee global optimality. Secondly, this approach overlooks the mutual effect between organic items and ads, resulting in an incomplete utilization of contextual information. Besides, it cannot prevent strategic behavior by advertisers. Therefore, we propose a context-aware integrated framework to address these issues. This framework applies automated mechanism design to integrated ad auctions for the first time. Specifically, it models ads and organic items simultaneously along with their contextual information and employs a learning-based approach to prevent advertisers from engaging in strategic behavior. Afterward, the framework directly generates a mixed list, enhancing the overall performance. We also propose Transformer encoder-based Integrated Contextual Net work (TICNet) to generate the optimal integrated contextual ad auction. Finally, we validate the effectiveness of TICNet on synthetic and real-world datasets. Our experimental results demonstrate that TICNet significantly outperforms baseline models across multiple metrics.

Automatic assessment of driver autonomic nervous system conditions is crucial for enhancing driving safety and healthcare. We present a novel approach that combines a dual optical fiber sensor system and a sophisticated deep learning framework, VHDP, with a strong emphasis on graph learning and spatiotemporal modeling techniques. The proposed fiber interferometer based dual optical fiber sensor system can effectively monitor driver vital signs in various environments. The VHDP framework, a significant innovation in deep learning, first utilizes the EMGLCN module to extract spatial and temporal features from the acquired heart rate variability (HRV) data for graph modeling. Then, through the dynamic spatial-temporal multi-graph method and the temporal-awareness attention module (TAA), it captures cross-time and dimensional correlations. Finally, the prior knowledge guided recalibration fusion module (PKGRF) generates accurate outputs. Experimental results show that the mean square error (MSE), mean absolute error (MAE) and R-square (R2) reach 2.354, 0.896 and 0.9857 respectively, outperforming state-of-the-art approaches. This work not only provides a new method for long-term driver HRV assessment and forecasting but also demonstrates the potential of graph learning and spatiotemporal modeling in the fields of medical monitoring and artificial intelligence, offering valuable insights for the development of portable vital signs monitoring devices in the context of the Internet of Medical Things (IoMT).

Coherent optical polarization-division multiplexing (PDM) is a promising technique to enhance spectral efficiency by transmitting data streams independently using orthogonal polarizations of light. However, in challenging conditions like lightning strikes, rotation of state of polarization (RSOP) due to Kerr and Faraday effects can cause significant signal distortions at speeds up to Mrad/s. In this paper, we propose a maximum likelihood (ML) and an expectation maximization (EM) estimator for the estimation of RSOP and laser phase noise (PN). The ML estimator utilizes pilot symbols for deriving explicit RSOP estimates, while the EM estimator iteratively refines estimates by incorporating unknown transmitted data as latent variables, which approximates the ML estimator’s performance while enhancing spectral efficiency. A decision-aided (DA) scheme is subsequently proposed for dynamic tracking of RSOP and PN, as well as signal detection. The Cramér-Rao lower bounds (CRLBs) are derived for performance evaluation, and simulation results demonstrate the superior RSOP tracking performance of our proposed estimators over conventional methods, with capable tracking speed beyond 100 Mrad/s. The proposed methods also exhibit robustness to impairments such as laser PN, polarization dependent loss (PDL), fiber nonlinearity and residual chromatic dispersion (CD).

In recent years, machine learning has demonstrated its potential in many challenging problems. In this paper, we extend its use to hardware formal property verification and propose DeepIC3, a method that takes advantage of graph learning in the classic IC3/PDR algorithm. In DeepIC3, graph neural networks are integrated to improve the result of local inductive generalization. This helps provide a global view of the state transition system and can potentially lead the algorithm out of local optima in the search of inductive invariants. Our experiments demonstrate that DeepIC3 accelerates the vanilla algorithm in nontrivial test cases of hardware model checking competition benchmarks (HWMCC2020) with up to 10. 8x speed-up. The proposed machine-learning integration preserves soundness and is universally applicable to various IC3/PDR implementations.

Thymic stromal lymphopoietin is a key cytokine involved in the pathogenesis of asthma and other allergic diseases. Targeting TSLP and its signaling pathways is increasingly recognized as an effective strategy for asthma treatment. This study focused on enhancing the affinity of the T6 antibody, which specifically targets TSLP, by integrating computational and experimental methods. The initial affinity of the T6 antibody for TSLP was lower than the benchmark antibody AMG157. To improve this, we utilized alanine scanning, molecular docking, and computational tools including mCSM-PPI2 and GEO-PPI to identify critical amino acid residues for site-directed mutagenesis. Subsequent mutations and experimental validations resulted in an antibody with significantly enhanced blocking capacity against TSLP. Our findings demonstrate the potential of computer-assisted techniques in expediting antibody affinity maturation, thereby reducing both the time and cost of experiments. The integration of computational methods with experimental approaches holds great promise for the development of targeted therapeutic antibodies for TSLP-related diseases.

Incomplete multi-view clustering (IMVC) aims to leverage complementary information from multi-view data with missing instances to enhance clustering performance. Many existing IMVC methods exhibit limitations in effectively exploiting hidden information and addressing distribution differences between views and modules. To address these challenges, we present a novel IMVC framework that leverages the proposed stack feature-based matrix completion to impute the missing instances, enhancing the exploitation of underlying information. We also incorporate graph consensus to integrate graph structures learned from both completed and observed data. Additionally, we introduce correntropy-induced metric as a flexible measurement to adaptively assign different constraints to various views and modules. Furthermore, we derive an efficient iterative algorithm based on Fenchel conjugate and accelerated block coordinate update (BCU) to solve the joint learning problem. Experimental results on eight benchmark datasets demonstrate the superior performance of our method compared to state-of-the-art IMVC methods across various metrics.

Coronavirus disease 2019 pandemic continues globally with a growing number of infections, but there are currently no effective antibody drugs against the virus. In addition, 90% amino acid sequence identity between the S2 subunit of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and SARS-CoV S proteins attracts us to examine S2-targeted cross-neutralizing antibodies that are not yet well defined. We therefore immunized RenMab mice with the full-length S protein and constructed a high-throughput antibody discovery method based on single-cell sequencing technology to isolate SARS-CoV-2 S-targeted neutralizing antibodies and cross-neutralizing antibodies against the S2 region of SARS-CoV-2/SARS-CoV S. Diversity of antibody sequences in RenMab mice and consistency in B-cell immune responses between RenMab mice and humans enabled screening of fully human virus-neutralizing antibodies. From all the frequency >1 paired clonotypes obtained from single-cell V(D)J sequencing, 215 antibodies with binding affinities were identified and primarily bound S2. However, only two receptor-binding domain-targeted clonotypes had neutralizing activity against SARS-CoV-2. Moreover, 5' single-cell RNA sequencing indicated that these sorted splenic B cells are mainly plasmablasts, germinal center (GC)-dependent memory B-cells and GC B-cells. Among them, plasmablasts and GC-dependent memory B-cells were considered the most significant possibility of producing virus-specific antibodies. Altogether, using a high-throughput single cell-based antibody discovery approach, our study highlighted the challenges of developing S2-binding neutralizing antibodies against SARS-CoV-2 and provided a novel direction for the enrichment of antigen-specific B-cells.

Channel estimation is the main hurdle to reaping the benefits promised by the intelligent reflecting surface (IRS), due to its absence of ability to transmit/receive pilot signals as well as the huge number of channel coefficients associated with its reflecting elements. Recently, a breakthrough was made in reducing the channel estimation overhead by revealing that the IRS-BS (base station) channels are common in the cascaded user-IRS-BS channels of all the users, and if the cascaded channel of one typical user is estimated, the other users' cascaded channels can be estimated very quickly based on their correlation with the typical user's channel [1]. One limitation of this strategy, however, is the waste of user energy, because many users need to keep silent when the typical user's channel is estimated. In this paper, we reveal another correlation hidden in the cascaded user-IRS-BS channels by observing that the user-IRS channel is common in all the cascaded channels from users to each BS antenna as well. Building upon this finding, we propose a novel two-phase channel estimation protocol in the uplink communication. Specifically, in Phase I, the correlation coefficients between the channels of a typical BS antenna and those of the other antennas are estimated; while in Phase II, the cascaded channel of the typical antenna is estimated. In particular, all the users can transmit throughput Phase I and Phase II. Under this strategy, it is theoretically shown that the minimum number of time instants required for perfect channel estimation is the same as that of the aforementioned strategy in the ideal case without BS noise. Then, in the case with BS noise, we show by simulation that the channel estimation error of our proposed scheme is significantly reduced thanks to the full exploitation of the user energy.

Visible light communication (VLC) system is a great candidate for indoor downlink access. Due to the limitation of light-emitting diode (LED)'s bandwidth, orthogonal frequency division multiplexing (OFDM) is used to enhance the transmission capacity of VLC system. Non-orthogonal multiple access (NOMA) can support multiple users in VLC system by sharing time and spectrum resources. The combination of OFDM and NOMA improves the overall channel capacity and spectral efficiency of a multi-user VLC system. In order to increase the data rate further, high order modulation formats are often applied. However, for the superposition of multiple high order modulation signals such as 16-quadrature amplitude modulation (QAM) and 64-QAM, it is hard to recover each user's signal under traditional successive interference cancellation (SIC) method, which is usually used in NOMA scheme. In this paper, we proposed a novel method to decode the superposed signals. This method is called ergodicity and comparison (EAC). By applying the EAC method, the bit error rate (BER) performance of two- user OFDM-NOMA VLC system is investigated under different power allocation conditions. In this system, the modulation formats for both the two users' signals are 4-QAM, 16-QAM and 64-QAM. Peak clipping effect on the received superposed signals is also considered. Numerical results demonstrate that the EAC method can improve significantly the BER performance of OFDM-NOMA VLC system, when high order M-QAM (M> 4) signals are applied to the two users.

Motivated by the recent work of [V. V. Vazirani, J. ACM, 59 (2012), 7], we take a fresh look at understanding the quality and robustness of solutions to Nash and nonsymmetric bargaining games by subjecting them to several stress tests. Our tests are quite basic; e.g., we ask whether the solutions are computable in polynomial time, and whether they have certain properties such as efficiency, fairness, and desirable response when agents change their disagreement points or play with a subset of the agents. Our main conclusion is that imposing submodularity, a natural economies of scale condition, on Nash and nonsymmetric bargaining games endows them with several desirable properties.

In this paper, we propose and demonstrate inband OSNR monitoring in 100-Gb/s PDM RZ-DQPSK system by using cost-effective low bandwidth sampling technique. The OSNR monitoring is insensitive to PMD effect.

We experimentally demonstrate optical signal to noise ratio (OSNR) and chromatic dispersion (CD) monitoring simultaneously in NRZ-DPSK and DQPSK systems by using single 2-dimension (2-D) phase portrait, which is generated by single-channel sampling technique.

We study the dynamic bid optimization problem via a primal dual approach. In the case we have no information about the distribution of queries, we reconstruct the ln(U=L) + 1 competitive algorithm proposed in [ZCL08] through a systematic way and showed the intuition behind this algorithm. In the case of random permutation model, we showed that the learning technique used in [DH09] can give us a (1 ¡ O(²)) competitive algorithm for any small constant ² > 0 as long as the optimum is large enough.

We study the scheduling problem on unrelated machines in the mechanism design setting. This problem was proposed and studied in the seminal paper (Nisan and Ronen 1999), where they gave a 1.75-approximation randomized truthful mechanism for the case of two machines. We improve this result by a 1.6737-approximation randomized truthful mechanism. We also generalize our result to a $0.8368m$-approximation mechanism for task scheduling with $m$ machines, which improve the previous best upper bound of $0.875m(Mu'alem and Schapira 2007).