AWEQ: Post-Training Quantization with Activation-Weight Equalization for Large Language Models

Large language models(LLMs) exhibit excellent performance across a variety of tasks, but they come with significant computational and storage costs. Quantizing these models is an effective way to alleviate this issue. However, existing methods struggle to strike a balance between model accuracy and hardware efficiency. This is where we introduce AWEQ, a post-training method that requires no additional training overhead. AWEQ excels in both ultra-low-bit quantization and 8-bit weight and activation (W8A8) quantization. There is an observation that weight quantization is less challenging than activation quantization. AWEQ transfers the difficulty of activation quantization to weights using channel equalization, achieving a balance between the quantization difficulties of both, and thereby maximizing performance. We have further refined the equalization method to mitigate quantization bias error, ensuring the robustness of the model. Extensive experiments on popular models such as LLaMA and OPT demonstrate that AWEQ outperforms all existing post-training quantization methods for large models

The resilience of interdependent transportation networks under targeted attack

Modern world builds on the resilience of interdependent infrastructures characterized as complex networks. Recently, a framework for analysis of interdependent networks has been developed to explain the mechanism of resilience in interdependent networks. Here we extend this interdependent network model by considering flows in the networks and study the system's resilience under different attack strategies. In our model, nodes may fail due to either overload or loss of interdependency. Under the interaction between these two failure mechanisms, it is shown that interdependent scale-free networks show extreme vulnerability. The resilience of interdependent SF networks is found in our simulation much smaller than single SF network or interdependent SF networks without flows.Comment: 5 pages, 4 figure

3D Flower-Like Hierarchitectures Constructed by SnS/SnS 2

Sn chalcogenides, including SnS, Sn2S3, and SnS2, have been extensively studied as anode materials for lithium batteries. In order to obtain one kind of high capacity, long cycle life lithium batteries anode materials, three-dimensional (3D) flower-like hierarchitectures constructed by SnS/SnS2 heterostructure nanosheets with thickness of ~20 nm have been synthesized via a simple one-pot solvothermal method. The obtained samples exhibit excellent electrochemical performance as anode for Li-ion batteries (LIBs), which deliver a first discharge capacity of 1277 mAhg−1 and remain a reversible capacity up to 500 mAhg−1 after 50 cycles at a current of 100 mAg−1

Electrical Control of Plasmon Resonance with Graphene

Surface plasmon, with its unique capability to concentrate light into sub-wavelength volume, has enabled great advances in photon science, ranging from nano-antenna and single-molecule Raman scattering to plasmonic waveguide and metamaterials. In many applications it is desirable to control the surface plasmon resonance in situ with electric field. Graphene, with its unique tunable optical properties, provides an ideal material to integrate with nanometallic structures for realizing such control. Here we demonstrate effective modulation of the plasmon resonance in a model system composed of hybrid graphene-gold nanorod structure. Upon electrical gating the strong optical transitions in graphene can be switched on and off, which leads to significant modulation of both the resonance frequency and quality factor of plasmon resonance in gold nanorods. Hybrid graphene-nanometallic structures, as exemplified by this combination of graphene and gold nanorod, provide a general and powerful way for electrical control of plasmon resonances. It holds promise for novel active optical devices and plasmonic circuits at the deep subwavelength scale

Coxsackievirus A6 Induces Cell Cycle Arrest in G0/G1 Phase for Viral Production

Recent epidemiological data indicate that outbreaks of hand, foot, and mouth disease (HFMD), which can be categorized according to its clinical symptoms as typical or atypical, have markedly increased worldwide. A primary causative agent for typical HFMD outbreaks, enterovirus 71 (EV71), has been shown to manipulate the cell cycle in S phase for own replication; however, it is not clear whether coxsackievirus (CVA6), the main agent for atypical HFMD, also regulates the host cell cycle. In this study, we demonstrate for the first time that CVA6 infection arrests the host cell cycle in G0/G1-phase. Furthermore, synchronization in G0/G1 phase, but not S phase or G2/M phase, promotes viral production. To investigate the mechanism of cell cycle arrest induced by CVA6 infection, we analyzed cell cycle progression after cell cycle synchronization at G0/G1 or G2/M. Our results demonstrate that CVA6 infection promotes G0/G1 phase entry from G2/M phase, and inhibits G0/G1 exit into S phase. In line with its role to arrest cells in G0/G1 phase, the expression of cyclinD1, CDK4, cyclinE1, CDK2, cyclinB1, CDK1, P53, P21, and P16 is regulated by CVA6. Finally, the non-structural proteins of CVA6, RNA-dependent RNA polymerase 3D and protease 3C , are demonstrated to be responsible for the G0/G1-phase arrest. These findings suggest that CVA6 infection arrested cell cycle in G0/G1-phase via non-structural proteins 3D and 3C, which may provide favorable environments for virus production

A Tunable Phonon-Exciton Fano System in Bilayer Graphene

Interference between different possible paths lies at the heart of quantum physics. Such interference between coupled discrete and continuum states of a system can profoundly change its interaction with light as seen in Fano resonance. Here we present a unique many-body Fano system composed of a discrete phonon vibration and continuous electron-hole pair transitions in bilayer graphene. Mediated by the electron-phonon interactions, the excited state is described by new quanta of elementary excitations of hybrid phonon-exciton nature. Infrared absorption of the hybrid states exhibit characteristic Fano lineshapes with parameters renormalized by many-body interactions. Remarkably, the Fano resonance in bilayer graphene is continuously tunable through electrical gating. Further control of the phonon-exciton coupling may be achieved with an optical field exploiting the excited state infrared activity. This tunable phonon-exciton system also offers the intriguing possibility of a 'phonon laser' with stimulated phonon amplification generated by population inversion of band-edge electrons.Comment: 21 pages, 3 figure

Vibration diagnosis method of oil pump inlet and outlet pipes

The vibration of oil pump set and its inlet and outlet pipes during their operation is one of the main threats to the safe and stable operation of pipeline. Herein, the source of excitation force and the vibration causes of pipeline system were analyzed, and the diagnosis method was put forward. In view of the vibration phenomenon of the oil pump inlet and outlet pipelines of a long distance pipeline, vibration detection and analysis were conducted for the oil pump and its inlet and outlet pipelines respectively by means of vibration test. For the maximum effective value of the vibration speed of the oil pump inlet and outlet pipelines was up to 9.28 mm/s and the peak speed was up to 13.12 mm/s, pipeline correction was required. Considering the factors such as the process parameters and vibration spectrum, it was found that the cause of pipeline vibration was the fluid pulse excitation generated by the oil pump. It was recommended to replace the pump with an impeller with smaller rated flow, so as to reduce the fluid pulse and further to eliminate the vibration excitation source of the pipeline system. After pump replacement, the maximum effective value of the vibration speed of pipeline was 3.19 mm/s and the peak speed was 4.51 mm/s. The results indicate that the vibration is significantly reduced, verify that the pipeline vibration is caused by the fluid pulse excitation produced by the oil pump, and guarantee the safe operation of oil pipeline

Semi-Supervised Adversarial Transfer Networks for Cross-Domain Intelligent Fault Diagnosis of Rolling Bearings

In recent advances, deep learning-based methods have been broadly applied in fault diagnosis, while most existing studies assume that source domain and target domain data follow the same distribution. As differences in operating conditions lead to the deterioration of diagnosis performance, domain adaptation technology has been introduced to bridge the distribution gap. However, most existing approaches generally assume that source domain labels are available under all health conditions during training, which is incompatible with the actual industrial situation. To this end, this paper proposes a semi-supervised adversarial transfer networks for cross-domain intelligent fault diagnosis of rolling bearings. Firstly, the Gramian Angular Field method is introduced to convert time domain vibration signals into images. Secondly, a semi-supervised learning-based label generating module is designed to generate artificial labels for unlabeled images. Finally, the dynamic adversarial transfer network is proposed to extract the domain-invariant features of all signal images and provide reliable diagnosis results. Two case studies were conducted on public rolling bearing datasets to evaluate the diagnostic performance. An experiment under variable operating conditions and an experiment with different numbers of source domain labels were carried out to verify the generalization and robustness of the proposed approach, respectively. Experiment results demonstrate that the proposed method can achieve high diagnosis accuracy when dealing with cross-domain tasks with deficient source domain labels, which may be more feasible in engineering applications than conventional methodologies