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The ${\rm SU}(8)$ was previously found to be the minimal simple gauge group where all three-generational Standard Model fermions can be non-trivially embedded, and it is maximally broken into ${\rm SU}(8)\to {\cal G}_{441}\equiv {\rm SU}(4)_s \otimes {\rm SU}(4)_W \otimes {\rm U}(1)_{X_0}$ at the GUT scale by the ${\rm SU}(8)$ adjoint Higgs field. Gauge symmetries in the strong and the weak sectors are extended by one and two ranks, respectively. The sequential strong-weak-weak (SWW) symmetry breaking stages were found to generate the observed hierarchical SM quark/lepton masses as well as the Cabibbo-Kobayashi-Maskawa (CKM) mixing pattern with the precise flavor identifications [1, 2]. We further study the possible weak-strong-weak (WSW) and weak-weak-strong (WWS) symmetry breaking patterns, and compare with the results that we have obtained by following the SWW sequence. The two-loop RGEs following both patterns are derived, where we cannot achieve the gauge coupling unification in the field theory framework. Based on these analyses, we suggest the gauge coupling unification to be interpreted in the context of the Ka{\v{c}}-Moody Lie algebra., Comment: 55 pages plus references, 3 figures, 21 tables. arXiv admin note: substantial text overlap with arXiv:2402.10471

Through heavily doping sulfur into an apatite framework, we synthesize a new blend mainly comprising variant apatite and covellite (copper sulfide). Magnetic measurement exhibits that significant diamagnetism appears at around 260 K and drops dramatically below 30 K implying coexistence of two superconducting phases. The upper critical magnetic field is larger than 1000 Oe at 250 K. Electric measurement manifests that the current-voltage curves deviate from the normal linear lineshape suggesting the presence of zero-resistance effect, and the critical current is around 50 $\mu$A at 140 K. These exotic magnetic and electric features strongly indicate these two components, variant apatite and covellite, individually trigger two superconducting phases at near-room and low temperatures., Comment: 15 pages, 4 figures

We study the renormalizable group equations (RGEs) of the extended strong and weak gauge couplings in an ${\rm SU}(8)$ theory, where three-generational SM fermions are non-trivially embedded. This framework was previously found to generate the observed SM quark/lepton mass hierarchies and the Cabibbo-Kobayashi-Maskawa mixing pattern through its maximally breaking pattern. The field theoretical two-loop RGEs can not achieve the gauge coupling unification with the minimal setup, unless additional adjoint Higgs fields as well as the gravity-induced $d=5$ term to the ${\rm SU}(8)$ field strength term are included., Comment: 38 pages with references, two appendices, 11 tables, 2 figures. Sequel to: arXiv:2307.07921, arXiv:2402.10471

By codoping sulfur and copper into lead apatite, the crystal grains are directionally stacked and the room-temperature resistivity is reduced from insulating to $2\times10^{-5}~\Omega\cdot$m. The resistance-temperature curve exhibits a nearly linear relationship at low temperature suggesting the presence of strange-metal phase, and a second-order phase transition is then observed at around 230~K during cooling the samples. A possible Meissner effect is present in dc magnetic measurements. Further hydrothermal lead-free synthesis results in smaller resistance and stronger diamagnetism, demonstrating the essential component might be sulfur-substituted copper apatite and the alkalis matter as well. A clear pathway towards superconductivity in this material is subsequently benchmarked., Comment: 12 pages, 4 figures

The origins of Standard Model (SM) quark/lepton masses and the Cabibbo-Kobayashi-Maskawa (CKM) mixing pattern are described in an ${\rm SU}(8)$ theory, where three-generational SM fermions transform differently. A realistic symmetry breaking pattern with three intermediate scales is described, along which all vectorlike electrically charged fermions become massive. A set of $d=5$ operators that explicitly break the emergent global symmetries are analyzed to generate the observed three-generational SM quark/lepton mass hierarchies as well as the CKM mixing pattern. All SM flavors are identified in the spectrum., Comment: 37 pages with references, one appendix, 8 tables. Sequel to: arXiv:2307.07921

Information acquisition from target perception represents the key enabling technology of the Internet of Automatic Vehicles (IoAV), which is essential for the decision-making and control operation of connected automatic vehicles (CAVs). Exploring target information involves multiple operations on data, e.g., wireless sensing (for data acquisition), communication (for data transmission), and computing (for data analysis), which all rely on the consumption of time-space-frequency-computing (TSFC) multi-domain resources. Due to the coupled resource sharing of sensing, communication, and computing procedures, the resource management of information-oriented IoAV is commonly formulated as a non-convex NP-hard problem. In this article, further combining the integrated sensing and communication (ISAC) and computing, we introduce the integrated sensing, communication, and computing (ISCC), wherein the TSFC resources are decoupled from the specific processes and shared universally among sensing, communication, and computing processes. Furthermore, the information-oriented resource trading platform (IRTP) is established, which transforms the problem of ISCC resource management into a resource-information substitution model. Finally, we embed the employment topology structure in IoAV into neural network architecture, taking advantage of the graph neural network (GNN) and multi-worker reinforcement learning, and propose the dynamic resource management strategy based on the asynchronous advantage GNN (A2GNN) algorithm, which can achieve the convergence both of information gain maximization and resource consumption minimization, realizing efficient information-oriented resource management., Comment: 8 pages, 4 figures, 2 tables

The high-performance generative artificial intelligence (GAI) represents the latest evolution of computational intelligence, while the blessing of future 6G networks also makes edge intelligence (EI) full of development potential. The inevitable encounter between GAI and EI can unleash new opportunities, where GAI's pre-training based on massive computing resources and large-scale unlabeled corpora can provide strong foundational knowledge for EI, while EI can harness fragmented computing resources to aggregate personalized knowledge for GAI. However, the natural contradictory features pose significant challenges to direct knowledge sharing. To address this, in this paper, we propose the GAI-oriented synthetical network (GaisNet), a collaborative cloud-edge-end intelligence framework that buffers contradiction leveraging data-free knowledge relay, where the bidirectional knowledge flow enables GAI's virtuous-cycle model fine-tuning and task inference, achieving mutualism between GAI and EI with seamless fusion and collaborative evolution. Experimental results demonstrate the effectiveness of the proposed mechanisms. Finally, we discuss the future challenges and directions in the interplay between GAI and EI., Comment: 11 pages, 8 figures, and 5 tables

The rapid expansion of AI-generated content (AIGC) reflects the iteration from assistive AI towards generative AI (GAI) with creativity. Meanwhile, the 6G networks will also evolve from the Internet-of-everything to the Internet-of-intelligence with hybrid heterogeneous network architectures. In the future, the interplay between GAI and the 6G will lead to new opportunities, where GAI can learn the knowledge of personalized data from the massive connected 6G end devices, while GAI's powerful generation ability can provide advanced network solutions for 6G network and provide 6G end devices with various AIGC services. However, they seem to be an odd couple, due to the contradiction of data and resources. To achieve a better-coordinated interplay between GAI and 6G, the GAI-native networks (GainNet), a GAI-oriented collaborative cloud-edge-end intelligence framework, is proposed in this paper. By deeply integrating GAI with 6G network design, GainNet realizes the positive closed-loop knowledge flow and sustainable-evolution GAI model optimization. On this basis, the GAI-oriented generic resource orchestration mechanism with integrated sensing, communication, and computing (GaiRom-ISCC) is proposed to guarantee the efficient operation of GainNet. Two simple case studies demonstrate the effectiveness and robustness of the proposed schemes. Finally, we envision the key challenges and future directions concerning the interplay between GAI models and 6G networks., Comment: 10 pages, 5 figures, 1 table

With copper-substituted lead apatite below room temperature, we observe diamagnetic dc magnetization under magnetic field of 25 Oe with remarkable bifurcation between zero-field-cooling and field-cooling measurements, and under 200 Oe it changes to be paramagnetism. A glassy memory effect is found during cooling. Typical hysteresis loops for superconductors are detected below 250 K, along with an asymmetry between forward and backward sweep of magnetic field. Our experiment suggests at room temperature the Meissner effect is possibly present in this material., Comment: 7 pages, 4 figures

Task scheduling as an effective strategy can improve application performance on computing resource-limited devices over distributed networks. However, existing evaluation mechanisms fail to depict the complexity of diverse applications, which involve dependencies among tasks, computing resource requirements, and multi-dimensional quality of service (QoS) constraints. Furthermore, traditional QoS-oriented task scheduling strategies struggle to meet the performance requirements without considering differences in satisfaction and acceptance of application, leading application failures and resource wastage. To tackle these issues, a quality of experience (QoE) cost model is designed to evaluate application completion, depicting the relationship among application satisfaction, communications, and computing resources in the distributed networks. Specifically, considering the sensitivity and preference of QoS, we model the different dimensional QoS degradation cost functions for dependent tasks, which are then integrated into the QoE cost model. Based on the QoE model, the dependent task scheduling problem is formulated as the minimization of overall QoE cost, aiming to improve the application performance in the distributed networks, which is proven Np-hard. Moreover, a heuristic Hierarchical Multi-queue Task Scheduling Algorithm (HMTSA) is proposed to address the QoE-oriented task scheduling problem among multiple dependent tasks, which utilizes hierarchical multiple queues to determine the optimal task execution order and location according to different dimensional QoS priorities. Finally, extensive experiments demonstrate that the proposed algorithm can significantly improve the satisfaction of applications.

While machine learning (ML) includes a valuable array of tools for analyzing biomedical data, significant time and expertise is required to assemble effective, rigorous, and unbiased pipelines. Automated ML (AutoML) tools seek to facilitate ML application by automating a subset of analysis pipeline elements. In this study we develop and validate a Simple, Transparent, End-to-end Automated Machine Learning Pipeline (STREAMLINE) and apply it to investigate the added utility of photography-based phenotypes for predicting obstructive sleep apnea (OSA); a common and underdiagnosed condition associated with a variety of health, economic, and safety consequences. STREAMLINE is designed to tackle biomedical binary classification tasks while adhering to best practices and accommodating complexity, scalability, reproducibility, customization, and model interpretation. Benchmarking analyses validated the efficacy of STREAMLINE across data simulations with increasingly complex patterns of association. Then we applied STREAMLINE to evaluate the utility of demographics (DEM), self-reported comorbidities (DX), symptoms (SYM), and photography-based craniofacial (CF) and intraoral (IO) anatomy measures in predicting any OSA or moderate/severe OSA using 3,111 participants from Sleep Apnea Global Interdisciplinary Consortium (SAGIC). OSA analyses identified a significant increase in ROC-AUC when adding CF to DEM+DX+SYM to predict moderate/severe OSA. A consistent but non-significant increase in PRC-AUC was observed with the addition of each subsequent feature set to predict any OSA, with CF and IO yielding minimal improvements. Application of STREAMLINE to OSA data suggests that CF features provide additional value in predicting moderate/severe OSA, but neither CF nor IO features meaningfully improved the prediction of any OSA beyond established demographics, comorbidity and symptom characteristics., Comment: 23 pages, 7 figures, 1 table, 1 supplemental information document (77 pages), and 7 ancillary files

Federated learning (FL) is a classic paradigm of 6G edge intelligence (EI), which alleviates privacy leaks and high communication pressure caused by traditional centralized data processing in the artificial intelligence of things (AIoT). The implementation of multimodal federated perception (MFP) services involves three sub-processes, including sensing-based multimodal data generation, communication-based model transmission, and computing-based model training, ultimately relying on available underlying multi-domain physical resources such as time, frequency, and computing power. How to reasonably coordinate the multi-domain resources scheduling among sensing, communication, and computing, therefore, is crucial to the MFP networks. To address the above issues, this paper investigates service-oriented resource management with integrated sensing, communication, and computing (ISCC). With the incentive mechanism of the MFP service market, the resources management problem is redefined as a social welfare maximization problem, where the idea of "expanding resources" and "reducing costs" is used to improve learning performance gain and reduce resource costs. Experimental results demonstrate the effectiveness and robustness of the proposed resource scheduling mechanisms.

By decoupling substrate resources, network virtualization (NV) is a promising solution for meeting diverse demands and ensuring differentiated quality of service (QoS). In particular, virtual network embedding (VNE) is a critical enabling technology that enhances the flexibility and scalability of network deployment by addressing the coupling of Internet processes and services. However, in the existing deep neural networks (DNNs)-based works, the black-box nature DNNs limits the analysis, development, and improvement of systems. For example, in the industrial Internet of Things (IIoT), there is a conflict between decision interpretability and the opacity of DNN-based methods. In recent times, interpretable deep learning (DL) represented by deep neuro fuzzy systems (DNFS) combined with fuzzy inference has shown promising interpretability to further exploit the hidden value in the data. Motivated by this, we propose a DNFS-based VNE algorithm that aims to provide an interpretable NV scheme. Specifically, data-driven convolutional neural networks (CNNs) are used as fuzzy implication operators to compute the embedding probabilities of candidate substrate nodes through entailment operations. And, the identified fuzzy rule patterns are cached into the weights by forward computation and gradient back-propagation (BP). Moreover, the fuzzy rule base is constructed based on Mamdani-type linguistic rules using linguistic labels. In addition, the DNFS-driven five-block structure-based policy network serves as the agent for deep reinforcement learning (DRL), which optimizes VNE decision-making through interaction with the environment. Finally, the effectiveness of evaluation indicators and fuzzy rules is verified by simulation experiments.

Monitoring mass accretion onto substellar objects provides insights into the geometry of the accretion flows. We use the Lulin One-meter Telescope to monitor H$\alpha$ emission from FU Tau B, a $\sim$19 $M_{\rm Jup}$ brown-dwarf companion at 5.7" (719 au) from the host star, for six consecutive nights. This is the longest continuous H$\alpha$ monitoring for a substellar companion near the deuterium-burning limit. We aim to investigate if accretion near the planetary regime could be rotationally modulated as suggested by magnetospheric accretion models. We find tentative evidence that H$\alpha$ mildly varies on hourly and daily timescales, though our sensitivity is not sufficient to definitively establish any rotational modulation. No burst-like events are detected, implying that accretion onto FU Tau B is overall stable during the time baseline and sampling windows over which it was observed. The primary star FU Tau A also exhibits H$\alpha$ variations over timescales from minutes to days. This program highlights the potential of monitoring accretion onto substellar objects with small telescopes., Comment: Published in AJ

The impact of thermally activated electrons on superconductivity within the realm of narrow energy gap semiconductors is investigated, unveiling the potential emergence of room-temperature superconductivity without the necessity for extensive doping. This novel mechanism suggests that thermal excitations can influence carrier concentrations significantly, thus influencing condensation and affecting superconductivity properties. In this paper, we explore how the energy gap width and temperature interact to shape carrier concentrations and subsequently impact the manifestation of superconducting behavior.

Predicting high temperature superconductors has long been a great challenge. A major difficulty is how to predict the transition temperature Tc of superconductors. Recently, progress in material informatics has led to a number of machine learning models predicting Tc, which greatly improves the efficiency of prediction. Unfortunately, prevailing models have not shown adequate physical rationality and generalization ability to find new high temperature superconductors, yet. In this work, in order to give a trustable prediction on the unexplored materials, we built a bond-sensitive graph neural network (BSGNN), which is optimized to process the information of chemical bond and electron interaction in the crystal lattice, to predict the Tc maximum of each type of superconducting materials. On the basis of the domain knowledge considered in the data preparation and algorithm design, our model revealed a relevance between the Tc-Tc maximum and chemical bonds. The results indicate that shorter bond length is favored by high Tc, which is in accordance with previous human experience. Moreover, it also shows that some specific chemical elements are favored by high Tc, which is beyond what human experts already knew. It gives a convenient guidance for searching high temperature superconductors in materials database, by ruling out the materials that could never have high Tc.

Quantum mechanics calculations of an electronic orbital coupling distribution of the new possible Room Temperature (RT) system of lead apatite Pb10(PO4)6O system show that the complex orbital coupling characteristics of the new system are similar to those of copper oxide and with multiple orbital interactions. The overall bandwidth is significantly larger than the current largest copper oxide system, which can also be compared to near RT high-pressure hydride systems. By applying a material dependence law relating overall bandwidth to superconducting critical temperature across various typical superconducting systems, we predict that the electronic structure of the new material can support achieving RT superconductivity in the condition of a flatter band., Comment: 6 pages, 3 figures

Viewpoint invariance remains challenging for visual recognition in the 3D world, as altering the viewing directions can significantly impact predictions for the same object. While substantial efforts have been dedicated to making neural networks invariant to 2D image translations and rotations, viewpoint invariance is rarely investigated. Motivated by the success of adversarial training in enhancing model robustness, we propose Viewpoint-Invariant Adversarial Training (VIAT) to improve the viewpoint robustness of image classifiers. Regarding viewpoint transformation as an attack, we formulate VIAT as a minimax optimization problem, where the inner maximization characterizes diverse adversarial viewpoints by learning a Gaussian mixture distribution based on the proposed attack method GMVFool. The outer minimization obtains a viewpoint-invariant classifier by minimizing the expected loss over the worst-case viewpoint distributions that can share the same one for different objects within the same category. Based on GMVFool, we contribute a large-scale dataset called ImageNet-V+ to benchmark viewpoint robustness. Experimental results show that VIAT significantly improves the viewpoint robustness of various image classifiers based on the diversity of adversarial viewpoints generated by GMVFool. Furthermore, we propose ViewRS, a certified viewpoint robustness method that provides a certified radius and accuracy to demonstrate the effectiveness of VIAT from the theoretical perspective., Comment: 14 pages, 12 figures. arXiv admin note: substantial text overlap with arXiv:2307.10235

Visual recognition models are not invariant to viewpoint changes in the 3D world, as different viewing directions can dramatically affect the predictions given the same object. Although many efforts have been devoted to making neural networks invariant to 2D image translations and rotations, viewpoint invariance is rarely investigated. As most models process images in the perspective view, it is challenging to impose invariance to 3D viewpoint changes based only on 2D inputs. Motivated by the success of adversarial training in promoting model robustness, we propose Viewpoint-Invariant Adversarial Training (VIAT) to improve viewpoint robustness of common image classifiers. By regarding viewpoint transformation as an attack, VIAT is formulated as a minimax optimization problem, where the inner maximization characterizes diverse adversarial viewpoints by learning a Gaussian mixture distribution based on a new attack GMVFool, while the outer minimization trains a viewpoint-invariant classifier by minimizing the expected loss over the worst-case adversarial viewpoint distributions. To further improve the generalization performance, a distribution sharing strategy is introduced leveraging the transferability of adversarial viewpoints across objects. Experiments validate the effectiveness of VIAT in improving the viewpoint robustness of various image classifiers based on the diversity of adversarial viewpoints generated by GMVFool., Comment: Accepted by ICCV 2023

We study the origin of the global $B-L$ symmetry in a class of flavor-unified theories with gauge groups of ${\rm SU}(N\geq 6)$. In particular, we focus on the ${\rm SU}(8)$ theory which can minimally embed three-generational SM fermions non-trivially. A reformulation of the third law for the flavor sector proposed by Georgi is useful to manifest the underlying global symmetries. The 't Hooft anomaly matching and the generalized neutrality conditions for Higgs fields play the key roles in defining the $B-L$ symmetry. Based on the global $B-L$ symmetry, we count the Higgs fields that can develop the VEVs and the massless sterile neutrinos in the ${\rm SU}(8)$ theory. We also prove that a global $B-L$ symmetry can always be defined in any ${\rm SU}(N\geq 6)$ theory when it is spontaneously broken to the SM gauge symmetry., Comment: 35 pages plus references, 11 tables, matches the published version

Voice Conversion (VC) converts the voice of a source speech to that of a target while maintaining the source's content. Speech can be mainly decomposed into four components: content, timbre, rhythm and pitch. Unfortunately, most related works only take into account content and timbre, which results in less natural speech. Some recent works are able to disentangle speech into several components, but they require laborious bottleneck tuning or various hand-crafted features, each assumed to contain disentangled speech information. In this paper, we propose a VC model that can automatically disentangle speech into four components using only two augmentation functions, without the requirement of multiple hand-crafted features or laborious bottleneck tuning. The proposed model is straightforward yet efficient, and the empirical results demonstrate that our model can achieve a better performance than the baseline, regarding disentanglement effectiveness and speech naturalness., Comment: Accepted by INTERSPEECH2023

By using band structure calculations of quantum mechanical theory, some important peaks of DoS (Density of States) were obtained and classified based on crystal structure laws of cuprate superconductivity. In particular, the orbital interactions of the in-plane and out-of-plane ions of the copper-oxygen plane were investigated. The position, half-width, and height of DOS peak features were collected for all 35 typical curate systems which have critical temperature maximum data from the works of literature. By training test of 7 common machine learning algorithms, the relationship between the Tc maximum values and these orbital interaction parameters were mined. It was found that the key features of the orbital interaction affecting the Tc maximum were not only the flat band but also a new interaction between core orbitals in a deeper energy band position.

Hyperspectral Image (HSI) classification is an important issue in remote sensing field with extensive applications in earth science. In recent years, a large number of deep learning-based HSI classification methods have been proposed. However, existing methods have limited ability to handle high-dimensional, highly redundant, and complex data, making it challenging to capture the spectral-spatial distributions of data and relationships between samples. To address this issue, we propose a generative framework for HSI classification with diffusion models (SpectralDiff) that effectively mines the distribution information of high-dimensional and highly redundant data by iteratively denoising and explicitly constructing the data generation process, thus better reflecting the relationships between samples. The framework consists of a spectral-spatial diffusion module, and an attention-based classification module. The spectral-spatial diffusion module adopts forward and reverse spectral-spatial diffusion processes to achieve adaptive construction of sample relationships without requiring prior knowledge of graphical structure or neighborhood information. It captures spectral-spatial distribution and contextual information of objects in HSI and mines unsupervised spectral-spatial diffusion features within the reverse diffusion process. Finally, these features are fed into the attention-based classification module for per-pixel classification. The diffusion features can facilitate cross-sample perception via reconstruction distribution, leading to improved classification performance. Experiments on three public HSI datasets demonstrate that the proposed method can achieve better performance than state-of-the-art methods. For the sake of reproducibility, the source code of SpectralDiff will be publicly available at https://github.com/chenning0115/SpectralDiff.

We carried out X-ray diffraction and Extended X-ray Absorption Fine Structure (EXAFS) studies to investigate the origin of the low lattice thermal conductivity in BiCuSeO, and the role of silver (Ag) doping in doped samples. BiCuSeO is a promising thermoelectric material with high thermoelectric efficiency, which is significantly enhanced by doping either single Ag dopants or dual dopants (Pb and Ag). We verified that the thermal displacement parameters associated with copper (Cu) are significantly large in undoped BiCuSeO. Ag dopant, which replaces Cu, was also found to have similarly large thermal displacement parameters, that remain large down to low temperatures. Our results point towards significant disorder on Cu-site in both undoped and doped BiCuSeO, which is retained by Ag dopant on replacing Cu. The disorder is observed to be localized on the Cu-site and seems to be independent of other atoms in the crystal structure. Our observation of the disorder, which could be either static or quasi-static, is consistent with a rattling mode scenario., Comment: 30 pages, 8 figures

Face recognition is a prevailing authentication solution in numerous biometric applications. Physical adversarial attacks, as an important surrogate, can identify the weaknesses of face recognition systems and evaluate their robustness before deployed. However, most existing physical attacks are either detectable readily or ineffective against commercial recognition systems. The goal of this work is to develop a more reliable technique that can carry out an end-to-end evaluation of adversarial robustness for commercial systems. It requires that this technique can simultaneously deceive black-box recognition models and evade defensive mechanisms. To fulfill this, we design adversarial textured 3D meshes (AT3D) with an elaborate topology on a human face, which can be 3D-printed and pasted on the attacker's face to evade the defenses. However, the mesh-based optimization regime calculates gradients in high-dimensional mesh space, and can be trapped into local optima with unsatisfactory transferability. To deviate from the mesh-based space, we propose to perturb the low-dimensional coefficient space based on 3D Morphable Model, which significantly improves black-box transferability meanwhile enjoying faster search efficiency and better visual quality. Extensive experiments in digital and physical scenarios show that our method effectively explores the security vulnerabilities of multiple popular commercial services, including three recognition APIs, four anti-spoofing APIs, two prevailing mobile phones and two automated access control systems.

We study a possible gauge symmetry breaking pattern in an ${\rm SU}(7)$ grand unified theory, which describes the mass origins of all electrically charged SM fermions of the second and the third generations. Two intermediate gauge symmetries of ${\cal G}_{341}\equiv {\rm SU}(3)_c \otimes {\rm SU}(4)_W \otimes {\rm U}(1)_{X_0}$ and ${\cal G}_{331}\equiv {\rm SU}(3)_c \otimes {\rm SU}(3)_W \otimes {\rm U}(1)_{X_1}$ arise above the electroweak scale. SM fermion mass hierarchies between two generations can be obtained through a generalized seesaw mechanism. The mechanism can be achieved with suppressed symmetry breaking VEVs from multiple Higgs fields that are necessary to avoid tadpole terms in the Higgs potential. Some general features of the ${\rm SU}(7)$ fermion spectrum will be described, which include the existence of vectorlike fermions, the tree-level flavor changing weak currents between the SM fermions and heavy partner fermions, and the flavor non-universality between different SM generations from the extended weak sector., Comment: 55 pages, 1 figure, 14 tables, preprint matches the published version

Rotational speed is one of the important metrics to be measured for calibrating the electric motors in manufacturing, monitoring engine during car repairing, faults detection on electrical appliance and etc. However, existing measurement techniques either require prohibitive hardware (e.g., high-speed camera) or are inconvenient to use in real-world application scenarios. In this paper, we propose, EV-Tach, an event-based tachometer via efficient dynamic vision sensing on mobile devices. EV-Tach is designed as a high-fidelity and convenient tachometer by introducing dynamic vision sensor as a new sensing modality to capture the high-speed rotation precisely under various real-world scenarios. By designing a series of signal processing algorithms bespoke for dynamic vision sensing on mobile devices, EV-Tach is able to extract the rotational speed accurately from the event stream produced by dynamic vision sensing on rotary targets. According to our extensive evaluations, the Relative Mean Absolute Error (RMAE) of EV-Tach is as low as 0.03% which is comparable to the state-of-the-art laser tachometer under fixed measurement mode. Moreover, EV-Tach is robust to subtle movement of user's hand, therefore, can be used as a handheld device, where the laser tachometer fails to produce reasonable results., Comment: 10 pages,13 figures

We investigate a data quality-aware dynamic client selection problem for multiple federated learning (FL) services in a wireless network, where each client offers dynamic datasets for the simultaneous training of multiple FL services, and each FL service demander has to pay for the clients under constrained monetary budgets. The problem is formalized as a non-cooperative Markov game over the training rounds. A multi-agent hybrid deep reinforcement learning-based algorithm is proposed to optimize the joint client selection and payment actions, while avoiding action conflicts. Simulation results indicate that our proposed algorithm can significantly improve training performance., Comment: 6 pages,8 figures

In collaboration with the Liaoning CDC, China, we propose a prediction system to predict the subsequent hospitalization of children with adverse reactions based on data on adverse events following immunization. We extracted multiple features from the data, and selected "hospitalization or not" as the target for classification. Since the data are imbalanced, we used various class-imbalance learning methods for training and improved the RUSBoost algorithm. Experimental results show that the improved RUSBoost has the highest Area Under the ROC Curve on the target among these algorithms. Additionally, we compared these class-imbalance learning methods with some common machine learning algorithms. We combined the improved RUSBoost with dynamic web resource development techniques to build an evaluation system with information entry and vaccination response prediction capabilities for relevant medical practitioners.

Though deep reinforcement learning (DRL) has obtained substantial success, it may encounter catastrophic failures due to the intrinsic uncertainty of both transition and observation. Most of the existing methods for safe reinforcement learning can only handle transition disturbance or observation disturbance since these two kinds of disturbance affect different parts of the agent; besides, the popular worst-case return may lead to overly pessimistic policies. To address these issues, we first theoretically prove that the performance degradation under transition disturbance and observation disturbance depends on a novel metric of Value Function Range (VFR), which corresponds to the gap in the value function between the best state and the worst state. Based on the analysis, we adopt conditional value-at-risk (CVaR) as an assessment of risk and propose a novel reinforcement learning algorithm of CVaR-Proximal-Policy-Optimization (CPPO) which formalizes the risk-sensitive constrained optimization problem by keeping its CVaR under a given threshold. Experimental results show that CPPO achieves a higher cumulative reward and is more robust against both observation and transition disturbances on a series of continuous control tasks in MuJoCo.

The ever-growing concerns regarding data privacy have led to a paradigm shift in machine learning (ML) architectures from centralized to distributed approaches, giving rise to federated learning (FL) and split learning (SL) as the two predominant privacy-preserving ML mechanisms. However,implementing FL or SL in device-to-device (D2D)-enabled heterogeneous networks with diverse clients presents substantial challenges, including architecture scalability and prolonged training delays. To address these challenges, this article introduces two innovative hybrid distributed ML architectures, namely, hybrid split FL (HSFL) and hybrid federated SL (HFSL). Such architectures combine the strengths of both FL and SL in D2D-enabled heterogeneous wireless networks. We provide a comprehensive analysis of the performance and advantages of HSFL and HFSL, while also highlighting open challenges for future exploration. We support our proposals with preliminary simulations using three datasets in non-independent and non-identically distributed settings, demonstrating the feasibility of our architectures. Our simulations reveal notable reductions in communication/computation costs and training delays as compared to conventional FL and SL., Comment: 8 pages,3 figures

Network Virtualization (NV) is an emerging network dynamic planning technique to overcome network rigidity. As its necessary challenge, Virtual Network Embedding (VNE) enhances the scalability and flexibility of the network by decoupling the resources and services of the underlying physical network. For the future multi-domain physical network modeling with the characteristics of dynamics, heterogeneity, privacy, and real-time, the existing related works perform satisfactorily. Federated learning (FL) jointly optimizes the network by sharing parameters among multiple parties and is widely employed in disputes over data privacy and data silos. Aiming at the NV challenge of multi-domain physical networks, this work is the first to propose using FL to model VNE, and presents a VNE architecture based on Horizontal Federated Learning (HFL) (HFL-VNE). Specifically, combined with the distributed training paradigm of FL, we deploy local servers in each physical domain, which can effectively focus on local features and reduce resource fragmentation. A global server is deployed to aggregate and share training parameters, which enhances local data privacy and significantly improves learning efficiency. Furthermore, we deploy the Deep Reinforcement Learning (DRL) model in each server to dynamically adjust and optimize the resource allocation of the multi-domain physical network. In DRL-assisted FL, HFL-VNE jointly optimizes decision-making through specific local and federated reward mechanisms and loss functions. Finally, the superiority of HFL-VNE is proved by combining simulation experiments and comparing it with related works.

Topological phases of matter have been extensively investigated in solid state materials and classical wave systems with integer dimensions. However, topological states in non-integer dimensions remain largely unexplored. Fractals, being nearly the same at different scales, are one of the intriguing complex geometries with non-integer dimensions. Here, we demonstrate acoustic Sierpi\'nski fractal topological insulators with unconventional higher-order topological phenomena via consistent theory and experiments. We discover abundant topological edge and corner states emerging in our acoustic systems due to the rich edge and corner boundaries inside the fractals. Interestingly, the numbers of the edge and corner states scale the same as the bulk states with the system size and the exponents coincide with the Hausdorff fractal dimension of the Sierpi\'nski carpet. Furthermore, the emergent corner states exhibit unconventional spectrum and wave patterns. Our study opens a pathway toward topological states in fractal geometries., Comment: 14 Pages, 4 Figures

We study a toy ${\rm SU}(6)$ model with the symmetry breaking pattern of the extended $331$ symmetry of ${\rm SU}(3)_c \otimes {\rm SU}(3)_W \otimes {\rm U}(1)_X$. A "fermion-Higgs mismatching" symmetry breaking pattern is proposed for more realistic model building. Within such symmetry breaking pattern, only one Higgs doublet develops vacuum expectation value for the spontaneous electroweak symmetry breaking, and gives tree-level top quark mass. A natural VEV splittings in the $331$ breaking Higgs fields gives tree-level masses to both bottom quark and tau lepton. The $125\,{\rm GeV}$ SM-like Higgs boson discovered at the LHC can have Yukawa couplings to bottom quark and tau lepton as in the SM prediction, and this suggests the $331$ symmetry breaking scale to be $\sim {\cal O}(10)\,{\rm TeV}$., Comment: 32 pages, 2 tables, one appendix, matches the published version

Recent studies have demonstrated the effectiveness of the combination of machine learning and logical reasoning, including data-driven logical reasoning, knowledge driven machine learning and abductive learning, in inventing advanced technologies for different artificial intelligence applications. One-step abductive multi-target learning (OSAMTL), an approach inspired by abductive learning, via simply combining machine learning and logical reasoning in a one-step balanced multi-target learning way, has as well shown its effectiveness in handling complex noisy labels of a single noisy sample in medical histopathology whole slide image analysis (MHWSIA). However, OSAMTL is not suitable for the situation where diverse noisy samples (DiNS) are provided for a learning task. In this paper, giving definition of DiNS, we propose one-step abductive multi-target learning with DiNS (OSAMTL-DiNS) to expand the original OSAMTL to handle complex noisy labels of DiNS. Applying OSAMTL-DiNS to tumour segmentation for breast cancer in MHWSIA, we show that OSAMTL-DiNS is able to enable various state-of-the-art approaches for learning from noisy labels to achieve more rational predictions. We released a model pre-trained with OSAMTL-DiNS for tumour segmentation in HE-stained pre-treatment biopsy images in breast cancer, which has been successfully applied as a pre-processing tool to extract tumour-associated stroma compartment for predicting the pathological complete response to neoadjuvant chemotherapy in breast cancer., Comment: The final published version (81 pages)

We extend the unitary groups beyond the ${\rm SU}(5)$ and ${\rm SU}(6)$ to look for possible grand unified theories that give rise to three-generational Standard Model fermions without the simple repetition. By demanding asymptotic free theories at short distances, we find gauge groups of ${\rm SU}(7)$, ${\rm SU}(8)$ and ${\rm SU}(9)$ together with their anomaly-free irreducible representations are such candidates. Two additional gauge groups of ${\rm SU}(10)$ and ${\rm SU}(11)$ can also achieve the generational structure without asymptotic freedom. We also find these models can solve the Peccei-Quinn (PQ) quality problem which is intrinsic in the axion models, with the leading PQ-breaking operators determined from the symmetry requirement., Comment: 20 pages, 6 tables, matches the published version

We propose an ${\rm SU}(6)$ unified theory that solves the strong ${\it CP}$ problem with a high-quality axion. This is achieved by an automatic global Peccei-Quinn ${\rm U}(1)_{\rm PQ}$ symmetry and the gauged discrete $\mathbb{Z}_{4{\cal R}}$ symmetry. With the axion mass predictions of $m_a \sim {\cal O}(10^{-3} ) - {\cal O}(0.1) \,{\rm eV}$, as well as a universal axion-photon coupling in the unification models, the QCD axion can be probed in the upcoming experiments of IAXO. An intermediate gauge symmetry breaking scale characterized by the axion decay constant is obtained by the Peccei-Quinn quality argument, which is likely to achieve a successful leptogenesis through the type-I seesaw mechanism. The Higgs sector at the electroweak scale is a type-II two-Higgs-doublet model. The gauge coupling unification is possible with the supersymmetric extension., Comment: 9 pages, 3 figures, 3 tables, matches the published version

Recent studies have shown that autoencoder-based models can achieve superior performance on anomaly detection tasks due to their excellent ability to fit complex data in an unsupervised manner. In this work, we propose a novel autoencoder-based model, named StackVAE-G that can significantly bring the efficiency and interpretability to multivariate time series anomaly detection. Specifically, we utilize the similarities across the time series channels by the stacking block-wise reconstruction with a weight-sharing scheme to reduce the size of learned models and also relieve the overfitting to unknown noises in the training data. We also leverage a graph learning module to learn a sparse adjacency matrix to explicitly capture the stable interrelation structure among multiple time series channels for the interpretable pattern reconstruction of interrelated channels. Combining these two modules, we introduce the stacking block-wise VAE (variational autoencoder) with GNN (graph neural network) model for multivariate time series anomaly detection. We conduct extensive experiments on three commonly used public datasets, showing that our model achieves comparable (even better) performance with the state-of-the-art modelsand meanwhile requires much less computation and memory cost. Furthermore, we demonstrate that the adjacency matrix learned by our model accurately captures the interrelation among multiple channels, and can provide valuable information for failure diagnosis applications., Comment: Accepted to AI Open

We study the anomalous muon and electron magnetic moments by introducing a scalar with CP-violating Yukawa couplings to the lepton sector. By fitting these two magnetic moments with the recent experimental measurements, we find that such a leptophilic scalar in the mass range of $\mathcal{O}(10)- \mathcal{O}(1000 )\,\rm GeV$ can be a possible source for the current experimental deviations from the Standard Model (SM) predictions, with $\mathcal{O}(0.1) - \mathcal{O}(1)$ Yukawa couplings. The current electron and muon EDM constraints to the general CP-violating Yukawa couplings are discussed. We propose to search such a leptophilic scalar mediated at the future high-luminosity LHC (HL-LHC) runs, as well as the high-energy lepton colliders, including the CEPC and the muon collider. Our results show that the leptophilic scalar in the mass range of $\mathcal{O}(10)- \mathcal{O}(1000 )\,\rm GeV$ can be fully probed by the future experimental searches at the HL-LHC and the lepton colliders at their early stages., Comment: minor corrections with references added, 17 pages, 9 figures

Due to the dominant position of deep learning (mostly deep neural networks) in various artificial intelligence applications, recently, ensemble learning based on deep neural networks (ensemble deep learning) has shown significant performances in improving the generalization of learning system. However, since modern deep neural networks usually have millions to billions of parameters, the time and space overheads for training multiple base deep learners and testing with the ensemble deep learner are far greater than that of traditional ensemble learning. Though several algorithms of fast ensemble deep learning have been proposed to promote the deployment of ensemble deep learning in some applications, further advances still need to be made for many applications in specific fields, where the developing time and computing resources are usually restricted or the data to be processed is of large dimensionality. An urgent problem needs to be solved is how to take the significant advantages of ensemble deep learning while reduce the required expenses so that many more applications in specific fields can benefit from it. For the alleviation of this problem, it is essential to know about how ensemble learning has developed under the era of deep learning. Thus, in this article, we present fundamental discussions focusing on data analyses of published works, methodologies, recent advances and unattainability of traditional ensemble learning and ensemble deep learning. We hope this article will be helpful to realize the intrinsic problems and technical challenges faced by future developments of ensemble learning under the era of deep learning., Comment: 47 pages, 8 figures, 15 tables

We theoretically implement some hyperparallel optical elements, including quantum single photon transistor, router, and dynamic random access memory (DRAM). The inevitable side leakage and the imperfect birefringence of the quantum dot (QD)-cavity mediates are taken into account, and unity fidelities of our optical elements can be achieved. The hyperparallel constructions are based on polarization and spatial degrees of freedom (DOFs) of the photon to increase the parallel efficiency, improve the capacity of channel, save the quantum resources, reduce the operation time, and decrease the environment noises. Moreover, the practical schemes are robust against the side leakage and the coupling strength limitation in the microcavities., Comment: 15 pages, 5 figures

An innumerable number of individual choices go into discovering a new book. There are unmistakably two groups of booklovers: those who like to search online, follow other people's latest readings, or simply react to a system's recommendations; and those who love to wander between library stacks, lose themselves behind bookstore shelves, or simply hide behind piles of (un)organized books. Depending on which group a person may fall into, there are two distinct and corresponding mediums that inform his or her choices: digital, that provides efficient retrieval of information online, and physical, a more tactile pursuit that leads to unexpected discoveries and promotes serendipity. How could we possibly bridge the gap between these seemingly disparate mediums into an integrated system that can amplify the benefits they both offer? In this paper, we present the BookVIS application, which uses book-related data and generates personalized visualizations to follow users in their quest for a new book. In this new redesigned version, the app brings associative visual connections to support intuitive exploration of easily retrieved digital information and its relationship with the physical book in hand. BookVIS keeps track of the user's reading preferences and generates a dataSelfie as an individual snapshot of a personal taste that grows over time. Usability testing has also been conducted and has demonstrated the app's ability to identify distinguishable patterns in readers' tastes that could be further used to communicate personal preferences in new "shelf-browsing" iterations. By efficiently supplementing the user's cognitive information needs while still supporting the spontaneity and enjoyment of the book browsing experience, BookVIS bridges the gap between real and online realms, and maximizes the engagement of personalized mobile visual clues., Comment: 9 pages, 8 figures

In this work we study the light Higgs scenario in the framework of two Higgs doublet model (2HDM). In this case the heavier CP-even Higgs boson $H$ in 2HDM is the SM-like Higgs with 125 GeV mass and a CP-even Higgs boson $h$ lighter than 125 GeV exhibits in the spectrum. We find that this scenario exists in the alignment limit of $\sin(\beta-\alpha)\simeq 0$ and is still allowed by the global fit of the 125 GeV Higgs and the direct searches for $H\to hh$ and $h\to \gamma\gamma$. The case of $M_hM_H/2$ case and simulate the pair production of the light Higgs boson at the LHC. The pair production is sensitive to the soft $\mathbb{Z}_2$ symmetry breaking term $m_{12}$ in the 2HDM potential through the Higgs self-couplings. We find that the future high-luminosity LHC can discover the $b\bar{b}\gamma\gamma$ signal in the region of $\tan\beta\lesssim 1$ and $0

We study the domain wall solutions in the general two-Higgs-doublet model (2HDM) with a CP-violating phase. The 2HDM with the spontaneouse CP violation is found to have domain wall solutions whose tensions are $\mathcal{O}(10^6)\,{\rm GeV}^3$, which are excluded by the Zel'dovich-Kobzarev-Okun bound. With the explicit CP-violating (CPV) terms as the so-called biased term in the scalar potential, domain walls can collapse in the early Universe. The sizes of the explicit CP violation can be constrained from the Big Bang nucleosynthesis. This constraint is converted to the CPV mixing of $\alpha_c$, and is mostly sensitive to the mass splittings between two heavy neutral Higgs bosons. We estimate the possible gravitational wave signals and the electric dipole moment (EDM) predictions due to the domain wall collapsing. It turns out that the peak spectrum of the GW from the domain wall collapsing cannot be probed in any future program. In contrast, the untenable regions with very tiny explicit CPV parameter in the Higgs potential has been partially excluded by the latest electron EDM measurements at the ACME-II and will be further confirmed or excluded by the future ACME-III projection., Comment: 33 pages, 8 figures, matches the published version

We study the CP domain walls and the consequent gravitational waves induced by the spontaneous breaking of the CP symmetry in the complex singlet extension to the Standard Model. We impose the constraints from the unitarity, stability and the global minimal of the vacuum solutions on the model parameter space. The CP domain wall profiles and tensions are obtained by numerically solving the relevant field equations. The explicit CP violation terms are then introduced to the potential as biased terms to make the domain walls unstable and collapse, The BBN bound on the magnitude of the energy bias is taken into account. To achieve sufficiently strong gravitational wave signals, the domain wall tension $\sigma$ is required to be at least $\sigma/{\rm TeV}^3 \sim \mathcal{O}(10^3)$. We find that the gravitational wave spectrum can be probed in the future SKA and/or DECIGO programs, when the typical mass scale is at least $\sim \mathcal{O}(10)$ TeV and the explicit CP violation terms are as small as $\mathcal{O}(10^{-29}) - \mathcal{O}(10^{-27}) $. The gravitational waves from collapsing domain walls thus provide a complementarity to the probe of extremely small CP violation at high-energy scale., Comment: 21 pages, 4 figures

With the advantages of Millimeter wave in wireless communication network, the coverage radius and inter-site distance can be further reduced, the ultra dense network (UDN) becomes the mainstream of future networks. The main challenge faced by UDN is the serious inter-site interference, which needs to be carefully addressed by joint user association and resource allocation methods. In this paper, we propose a multi-agent Q-learning based method to jointly optimize the user association and resource allocation in UDN. The deep Q-network is applied to guarantee the convergence of the proposed method. Simulation results reveal the effectiveness of the proposed method and different performances under different simulation parameters are evaluated., Comment: 5 pages, 5 figures

Hyper-parallel quantum information processing is a promising and beneficial research field. In this paper, we present a method to implement a hyper-parallel controlled-phase-flip (hyper-CPF) gate for frequency-, spatial-, and time-bin-encoded qubits by coupling flying photons to trapped nitrogen vacancy (NV) defect centers. The scheme, which differs from their conventional parallel counterparts, is specifically advantageous in decreasing against the dissipate noise, increasing the quantum channel capacity, and reducing the quantum resource overhead. The gate qubits with frequency, spatial, and time-bin degrees of freedom (DOF) are immune to quantum decoherence in optical fibers, whereas the polarization photons are easily disturbed by the ambient noise., Comment: 11 pages, 3 figures

A superstructure can elicit versatile new properties of materials by breaking their original geometrical symmetries. It is an important topic in the layered graphene-like two-dimensional transition-metal dichalcogenides (TMDs), but its origin remains unclear. Using diamond-anvil cell techniques, synchrotron x-ray diffraction, x-ray absorption, and the first-principles calculations, we show that the evolution from the weak Van der Waals bonding to the Heisenberg covalent bonding between layers induces an isostructural transition in quasi-two-dimensional 1$\textit{T}$-type VSe$_2$ at high pressure. Furthermore, our results show that high-pressure induce a novel superstructure at 15.5 GPa, rather than suppress as it would normally, which is unexpected. It is driven by the Fermi surface nesting, enhanced by the pressure-induced distortion. The results suggest that the superstructure not only appears in the two-dimensional structure but also can emerge in the pressure-tuned three-dimensional structure with new symmetry and develop superconductivity., Comment: 17 pages, 5 figures

Towards automated retinal screening, this paper makes an endeavor to simultaneously achieve pixel-level retinal lesion segmentation and image-level disease classification. Such a multi-task approach is crucial for accurate and clinically interpretable disease diagnosis. Prior art is insufficient due to three challenges, i.e., lesions lacking objective boundaries, clinical importance of lesions irrelevant to their size, and the lack of one-to-one correspondence between lesion and disease classes. This paper attacks the three challenges in the context of diabetic retinopathy (DR) grading. We propose Lesion-Net, a new variant of fully convolutional networks, with its expansive path re-designed to tackle the first challenge. A dual Dice loss that leverages both semantic segmentation and image classification losses is introduced to resolve the second challenge. Lastly, we build a multi-task network that employs Lesion-Net as a side-attention branch for both DR grading and result interpretation. A set of 12K fundus images is manually segmented by 45 ophthalmologists for 8 DR-related lesions, resulting in 290K manual segments in total. Extensive experiments on this large-scale dataset show that our proposed approach surpasses the prior art for multiple tasks including lesion segmentation, lesion classification and DR grading, Comment: Accepted at ICPR 2020

We explore the extent to which future precision measurements of the Standard Model (SM) observables at the proposed $Z$-factories and Higgs factories may have impacts on new physics beyond the Standard Model, as illustrated by studying the Type-I Two-Higgs-doublet model (Type-I 2HDM). We include the contributions from the heavy Higgs bosons at the tree-level and at the one-loop level in a full model-parameter space. While only small $\tan\beta$ region is strongly constrained at tree level, the large $\tan\beta$ region gets constrained at loop level due to $\tan\beta$ enhanced tri-Higgs couplings. We perform a multiple variable global fit with non-alignment and non-degenerate masses. We find that the allowed parameter ranges could be tightly constrained by the future Higgs precision measurements, especially for small and large values of $\tan\beta$. Indirect limits on the masses of heavy Higgs bosons can be obtained, which can be complementary to the direct searches of the heavy Higgs bosons at hadron colliders. We also find that the expected accuracies at the $Z$-pole and at a Higgs factory are quite complementary in constraining mass splittings of heavy Higgs bosons. The typical results are $|\cos(\beta-\alpha)| < 0.05, |\Delta m_\Phi | < 200\ {\rm GeV}$, and $\tan\beta \gtrsim 0.3$. The reaches from CEPC, FCCee and ILC are also compared, for both Higgs and $Z$-pole precision measurements. Comparing to the Type-II 2HDM, the 95\% C.L. allowed range of $\cos(\beta-\alpha)$ is larger, especially for large values of $\tan\beta$., Comment: Delete the duplicated plots. arXiv admin note: text overlap with arXiv:1808.02037