arXiv subjects

Ning Kang

Publications and source records attributed to Ning Kang.

At least 19 recordsLinked to original sources

arXiv2026

Symmetry Origins of the Field-Free Superconducting Diode Effect in the Kagome Superconductor CsV$_3$Sb$_5$

Field-free superconducting diode effects require both inversion-symmetry breaking and an internal time-reversal-symmetry (TRS) breaking field, making them sensitive probes of hidden order in superconductors. In centrosymmetric kagome AV$_3$Sb$_5$, the inversion symmetry generally should generally preclude the observation of the superconducting diode effect. Furthermore, though TRS breaking has been reported in the superconducting regime of CsV$_3$Sb$_5$, whether it is generated by superconductivity or inherited from charge-density-wave (CDW) order remains unresolved. Here we show that pristine CsV$_3$Sb$_5$ devices exhibit no intrinsic field-free superconducting diode effect, whereas surface oxidation or asymmetric etching activates a large nonreciprocal supercurrent. Moreover, the response is stochastic, with sweep-dependent polarity and magnitude, indicating metastable TRS-breaking domain configurations. Small out-of-plane magnetic fields stabilize the superconducting diode response, consistent with field selection of such domains. Finally, when long-range CDW order is suppressed by Ti doping, the SDE disappears. Our results establish the symmetry requirements for the field-free SDE in CsV$_3$Sb$_5$, reveal its stochastic domain-controlled character, and link superconducting-state TRS breaking to CDW-related order.

cond-mat.supr-con↗

arXiv2026

Efficient hot electron generation via low-coherence lasers

Hot electrons generated in laser-produced plasmas are a central focus in inertial confinement fusion, laboratory astrophysics, and high-energy-density physics. These electrons originate from instabilities in nonlinear laser-plasma interactions, which are critically modulated by laser bandwidth. Here, we experimentally demonstrate enhanced generation of hot electrons by utilizing instantaneous low-coherence lasers with two bandwidths (0.2% and 0.6%) at intensities of 2-8x10^{14} W/cm^2 and energies up to 620 J. A significant enhancement of hot electron temperature and hard X-ray yield is observed with the broadband lasers compared to a conventional narrowband laser. The results show that the hot electron energy conversion efficiency of the 0.6% broadband laser is approximately 4 times higher than that of the narrowband laser, reaching a maximum value of 2.8%. These findings validate a moderate-bandwidth laser as an efficient hot electron source and support the generation of bright X-ray sources for advanced imaging in high-energy-density physics.

physics.plasm-ph↗

arXiv2025

Switchable half-quantum flux states in a ring of the kagome superconductor CsV$_3$Sb$_5$

Magnetic flux quantization in units of $\Phi_0 = h/2e$ is a defining feature of superconductivity, rooted in the charge-2e nature of Cooper pairs. In a ring geometry, the flux quantization leads to oscillations in the critical temperature with magnetic flux, known as the Little-Parks effect. While the maximal critical temperature is conventionally at zero flux, departures from this rule, for instance shifts by a half-quantum flux $\Phi_0/2$, clearly signal unconventional superconducting states and require sign-changing order parameters. Historically, such $\pi$-phase shifts in Little-Parks oscillations have been found in tricrystals or engineered ring structures that intentionally incorporate a $\pi$-phase shift. Here we report the discovery of switchable half-quantum flux states in rings made from single crystals of the kagome superconductor CsV$_3$Sb$_5$. We observe Little-Parks oscillations with a $\pi$-phase shift at zero bias current, which can be reversibly tuned to conventional Little-Parks oscillations upon applying a bias current. Between the $\pi$-phase and 0-phase regimes, $h/4e$ periodic oscillations appear. Our observations suggest unconventional pairing, potentially in the form of a multicomponent order parameter in the kagome superconductor CsV$_3$Sb$_5$, and reveal an electrically tunable landscape of competing superconducting condensates and fractional flux states.

cond-mat.supr-con↗

arXiv2025

FUDOKI: Discrete Flow-based Unified Understanding and Generation via Kinetic-Optimal Velocities

The rapid progress of large language models (LLMs) has catalyzed the emergence of multimodal large language models (MLLMs) that unify visual understanding and image generation within a single framework. However, most existing MLLMs rely on autoregressive (AR) architectures, which impose inherent limitations on future development, such as the raster-scan order in image generation and restricted reasoning abilities in causal context modeling. In this work, we challenge the dominance of AR-based approaches by introducing FUDOKI, a unified multimodal model purely based on discrete flow matching, as an alternative to conventional AR paradigms. By leveraging metric-induced probability paths with kinetic optimal velocities, our framework goes beyond the previous masking-based corruption process, enabling iterative refinement with self-correction capability and richer bidirectional context integration during generation. To mitigate the high cost of training from scratch, we initialize FUDOKI from pre-trained AR-based MLLMs and adaptively transition to the discrete flow matching paradigm. Experimental results show that FUDOKI achieves performance comparable to state-of-the-art AR-based MLLMs across both visual understanding and image generation tasks, highlighting its potential as a foundation for next-generation unified multimodal models. Furthermore, we show that applying test-time scaling techniques to FUDOKI yields significant performance gains, further underscoring its promise for future enhancement through reinforcement learning.

cs.CV↗

arXiv2025

Organometallic-Inorganic Hybrid MXenes with Tunable Superconductivity

Ti-based two-dimensional transition-metal carbides (MXenes) have attracted attention due to their superior properties and are being explored across various applications1,2. Despite their versatile properties, superconductivity has never been demonstrated, not even predicted, for this important group of 2D materials. In this work, we have introduced an electrochemical intercalation protocol to construct versatile organometallic-inorganic hybrid MXenes and achieved tunable superconductivity in the metallocene-modified layered crystals. Through structural editing of MXene matrix at atomic scale and meticulously modulated intercalation route, Ti3C2Tx intercalated with metallocene species exhibits a superconductive transition temperature (Tc) of 10.2 K. Guest intercalation induced electron filling and strain engineering are responsible for the emerging superconductivity in this intrinsically non-superconducting material. Theoretically, simulated electron-phonon interaction effects further elucidate the nature of the changes in Tc. Furthermore, the Tc of crafted artificial superlattices beyond Ti-based MXenes have been predicted, offering a general strategy for engineering superconductivity and magnetism in layered hybrid materials.

cond-mat.supr-con↗

arXiv2025

LiT: Delving into a Simple Linear Diffusion Transformer for Image Generation

In this paper, we investigate how to convert a pre-trained Diffusion Transformer (DiT) into a linear DiT, as its simplicity, parallelism, and efficiency for image generation. Through detailed exploration, we offer a suite of ready-to-use solutions, ranging from linear attention design to optimization strategies. Our core contributions include 5 practical guidelines: 1) Applying depth-wise convolution within simple linear attention is sufficient for image generation. 2) Using fewer heads in linear attention provides a free-lunch performance boost without increasing latency. 3) Inheriting weights from a fully converged, pre-trained DiT. 4) Loading all parameters except those related to linear attention. 5) Hybrid knowledge distillation: using a pre-trained teacher DiT to help the training of the student linear DiT, supervising not only the predicted noise but also the variance of the reverse diffusion process. These guidelines lead to our proposed \underline{L}inear D\underline{i}ffusion \underline{T}ransformer (LiT), which serves as a safe and efficient alternative baseline for DiT with pure linear attention. In class-conditional 256$\times$256 and 512$\times$512 ImageNet generation, LiT can be quickly adapted from DiT using only $20\%$ and $33\%$ of DiT's training steps, respectively, while achieving comparable performance. LiT also rivals methods based on Mamba or Gated Linear Attention. Moreover, the same guidelines generalize to text-to-image generation: LiT can be swiftly converted from PixArt-$\Sigma$ to generate high-quality images, maintaining comparable GenEval scores.

cs.CV↗

arXiv2024

JingZhao: A Framework for Rapid NIC Prototyping in the Domain-Specific-Network Era

The network is becoming domain-specific, which requires on-demand design of the network protocols, as well as the microarchitecture of the NIC. However, to develop such a NIC is not that easy. Since the scissor gap between network speed and the growth of CPU frequency is expanding, most of the protocols need to be offloaded to hardware. The process of designing, verifying and optimizing a domain-specific NIC usually takes great effort, which hinders the rapid iteration of new protocols and algorithms. In this paper, we propose JingZhao, an open-sourced framework for NIC prototyping, which could be leveraged to rapidly implement and verify a domain-specific NIC. Using this framework, we implement a fully-functional RDMA NIC (RNIC). To the best of our knowledge, this represents the first open-source RDMA NIC solution with complete compatibility to the standard OFED communication library. The RNIC was also taped out using TSMC's 28nm process to validate our design. Our evaluation results show that new network functions can be easily integrated into the framework, and achieve nearly line-rate packet processing.

cs.NI↗

arXiv2024

Signatures of spin-polarized p-wave superconductivity in the kagome material RbV$_3$Sb$_5$

The study of kagome materials has attracted much attention in the past few years due to the presence of many electron-electron interaction-driven phases in a single material. These include charge density waves, nematic phases, superconducting phases, and pair density waves. In this work, we report the discovery of intrinsic spin-polarized p-wave superconductivity in the thin-flake kagome material RbV$_3$Sb$_5$. Firstly, when an in-plane magnetic field is swept in opposite directions, we observe a unique form of hysteresis in magnetoresistance which is different from the hysteresis induced by extrinsic mechanisms such as flux-trapping or superheating and supercooling effects. The unconventional hysteresis indicates the emergence of an intrinsic time-reversal symmetry-breaking superconducting phase. Strikingly, at a fixed magnetic field, the finite-resistance state can be transitioned into the superconducting state by applying and subsequently removing a large current. Secondly, at temperatures around 400 mK, the re-entrance of superconductivity occurs during an in-plane field-sweeping process. This kind of re-entrance is asymmetric about the zero field axis and observed in all field directions for a fixed current direction, which is different from the re-entrance observed in conventional superconductors. These findings put very strong constraints on the possible superconducting pairing symmetry of RbV$_3$Sb$_5$. We point out that the pairing symmetry, which is consistent with the crystal symmetry and all the observed novel properties, is possibly a time-reversal symmetry-breaking, p-wave pairing with net spin polarization. Importantly, this p-wave pairing gives rise to a nodal topological superconducting state with Majorana flat bands on the sample edges.

cond-mat.str-el↗

arXiv2024

FNCC: Fast Notification Congestion Control in Data Center Networks

Congestion control plays a pivotal role in large-scale data centers, facilitating ultra-low latency, high bandwidth, and optimal utilization. Even with the deployment of data center congestion control mechanisms such as DCQCN and HPCC, these algorithms often respond to congestion sluggishly. This sluggishness is primarily due to the slow notification of congestion. It takes almost one round-trip time (RTT) for the congestion information to reach the sender. In this paper, we introduce the Fast Notification Congestion Control (FNCC) mechanism, which achieves sub-RTT notification. FNCC leverages the acknowledgment packet (ACK) from the return path to carry in-network telemetry (INT) information of the request path, offering the sender more timely and accurate INT. To further accelerate the responsiveness of last-hop congestion control, we propose that the receiver notifies the sender of the number of concurrent congested flows, which can be used to adjust the congested flows to a fair rate quickly. Our experimental results demonstrate that FNCC reduces flow completion time by 27.4% and 88.9% compared to HPCC and DCQCN, respectively. Moreover, FNCC triggers minimal pause frames and maintains high utilization even at 400Gbps.

cs.NI↗

arXiv2023

New Observations of Solar Wind 1/f Turbulence Spectrum from Parker Solar Probe

The trace magnetic power spectrum in the solar wind is known to be characterized by a double power law at scales much larger than the proton gyro-radius, with flatter spectral exponents close to -1 found at the lower frequencies below an inertial range with indices closer to $[-1.5,-1.6]$. The origin of the $1/f$ range is still under debate. In this study, we selected 109 magnetically incompressible solar wind intervals ($δ|\boldsymbol B|/|\boldsymbol B| \ll 1$) from Parker Solar Probe encounters 1 to 13 which display such double power laws, with the aim of understanding the statistics and radial evolution of the low frequency power spectral exponents from Alfvén point up to 0.3 AU. New observations from closer to the sun show that in the low frequency range solar wind turbulence can display spectra much shallower than $1/f$, evolving asymptotically to $1/f$ as advection time increases, indicating a dynamic origin for the $1/f$ range formation. We discuss the implications of this result on the Matteini et al. (2018) conjecture for the $1/f$ origin as well as example spectra displaying a triple power law consistent with the model proposed by Chandran et al. (2018), supporting the dynamic role of parametric decay in the young solar wind. Our results provide new constraints on the origin of the $1/f$ spectrum and further show the possibility of the coexistence of multiple formation mechanisms.

astro-ph.SR↗

arXiv2022

Parallel Neural Local Lossless Compression

The recently proposed Neural Local Lossless Compression (NeLLoC), which is based on a local autoregressive model, has achieved state-of-the-art (SOTA) out-of-distribution (OOD) generalization performance in the image compression task. In addition to the encouragement of OOD generalization, the local model also allows parallel inference in the decoding stage. In this paper, we propose two parallelization schemes for local autoregressive models. We discuss the practicalities of implementing the schemes and provide experimental evidence of significant gains in compression runtime compared to the previous, non-parallel implementation.

eess.IV↗

arXiv2022

PILC: Practical Image Lossless Compression with an End-to-end GPU Oriented Neural Framework

Generative model based image lossless compression algorithms have seen a great success in improving compression ratio. However, the throughput for most of them is less than 1 MB/s even with the most advanced AI accelerated chips, preventing them from most real-world applications, which often require 100 MB/s. In this paper, we propose PILC, an end-to-end image lossless compression framework that achieves 200 MB/s for both compression and decompression with a single NVIDIA Tesla V100 GPU, 10 times faster than the most efficient one before. To obtain this result, we first develop an AI codec that combines auto-regressive model and VQ-VAE which performs well in lightweight setting, then we design a low complexity entropy coder that works well with our codec. Experiments show that our framework compresses better than PNG by a margin of 30% in multiple datasets. We believe this is an important step to bring AI compression forward to commercial use.

eess.IV↗

arXiv2022

Asymmetric Fraunhofer pattern in Josephson junctions from heterodimensional superlattice V$_5$S$_8$

Introduction of spin-orbit coupling (SOC) in a Josephson junction (JJ) gives rise to unusual Josephson effects. We investigate JJs based on a newly discovered heterodimensional superlattice V$_5$S$_8$ with a special form of SOC. The unique homointerface of our JJs enables elimination of extrinsic effects due to interfaces and disorder. We observe asymmetric Fraunhofer patterns with respect to both the perpendicular magnetic field and the current. The asymmetry is influenced by an in-plane magnetic field. Analysis of the pattern points to a nontrivial spatial distribution of the Josephson current that is intrinsic to the SOC in V$_5$S$_8$.

cond-mat.supr-con↗

arXiv2022

Split Hierarchical Variational Compression

Variational autoencoders (VAEs) have witnessed great success in performing the compression of image datasets. This success, made possible by the bits-back coding framework, has produced competitive compression performance across many benchmarks. However, despite this, VAE architectures are currently limited by a combination of coding practicalities and compression ratios. That is, not only do state-of-the-art methods, such as normalizing flows, often demonstrate out-performance, but the initial bits required in coding makes single and parallel image compression challenging. To remedy this, we introduce Split Hierarchical Variational Compression (SHVC). SHVC introduces two novelties. Firstly, we propose an efficient autoregressive prior, the autoregressive sub-pixel convolution, that allows a generalisation between per-pixel autoregressions and fully factorised probability models. Secondly, we define our coding framework, the autoregressive initial bits, that flexibly supports parallel coding and avoids -- for the first time -- many of the practicalities commonly associated with bits-back coding. In our experiments, we demonstrate SHVC is able to achieve state-of-the-art compression performance across full-resolution lossless image compression tasks, with up to 100x fewer model parameters than competing VAE approaches.

eess.IV↗

arXiv2022

Homointerface planar Josephson junction based on inverse proximity effect

The quality of a superconductor-normal metal-superconductor Josephson junction (JJ) depends crucially on the transparency of the superconductor-normal metal (S/N) interface. We demonstrate a technique for fabricating planar JJs with perfect S/N interfaces. The technique utilizes a strong inverse proximity effect discovered in Al/V$_5$S$_8$ bilayers, by which the Al layer is driven into the resistive state. The highly transparent S/N homointerface and the peculiar normal metal enable the flow of Josephson supercurrent across a 2.9 $μ$m long weak link. Moreover, our JJ exhibits a giant critical current and a large product of the critical current and the normal state resistance.

cond-mat.supr-con↗

arXiv2021

iVPF: Numerical Invertible Volume Preserving Flow for Efficient Lossless Compression

It is nontrivial to store rapidly growing big data nowadays, which demands high-performance lossless compression techniques. Likelihood-based generative models have witnessed their success on lossless compression, where flow based models are desirable in allowing exact data likelihood optimisation with bijective mappings. However, common continuous flows are in contradiction with the discreteness of coding schemes, which requires either 1) imposing strict constraints on flow models that degrades the performance or 2) coding numerous bijective mapping errors which reduces the efficiency. In this paper, we investigate volume preserving flows for lossless compression and show that a bijective mapping without error is possible. We propose Numerical Invertible Volume Preserving Flow (iVPF) which is derived from the general volume preserving flows. By introducing novel computation algorithms on flow models, an exact bijective mapping is achieved without any numerical error. We also propose a lossless compression algorithm based on iVPF. Experiments on various datasets show that the algorithm based on iVPF achieves state-of-the-art compression ratio over lightweight compression algorithms.

cs.LG↗

arXiv2021

iFlow: Numerically Invertible Flows for Efficient Lossless Compression via a Uniform Coder

It was estimated that the world produced $59 ZB$ ($5.9 \times 10^{13} GB$) of data in 2020, resulting in the enormous costs of both data storage and transmission. Fortunately, recent advances in deep generative models have spearheaded a new class of so-called "neural compression" algorithms, which significantly outperform traditional codecs in terms of compression ratio. Unfortunately, the application of neural compression garners little commercial interest due to its limited bandwidth; therefore, developing highly efficient frameworks is of critical practical importance. In this paper, we discuss lossless compression using normalizing flows which have demonstrated a great capacity for achieving high compression ratios. As such, we introduce iFlow, a new method for achieving efficient lossless compression. We first propose Modular Scale Transform (MST) and a novel family of numerically invertible flow transformations based on MST. Then we introduce the Uniform Base Conversion System (UBCS), a fast uniform-distribution codec incorporated into iFlow, enabling efficient compression. iFlow achieves state-of-the-art compression ratios and is $5\times$ quicker than other high-performance schemes. Furthermore, the techniques presented in this paper can be used to accelerate coding time for a broad class of flow-based algorithms.

cs.LG↗

arXiv2021

NASOA: Towards Faster Task-oriented Online Fine-tuning with a Zoo of Models

Fine-tuning from pre-trained ImageNet models has been a simple, effective, and popular approach for various computer vision tasks. The common practice of fine-tuning is to adopt a default hyperparameter setting with a fixed pre-trained model, while both of them are not optimized for specific tasks and time constraints. Moreover, in cloud computing or GPU clusters where the tasks arrive sequentially in a stream, faster online fine-tuning is a more desired and realistic strategy for saving money, energy consumption, and CO2 emission. In this paper, we propose a joint Neural Architecture Search and Online Adaption framework named NASOA towards a faster task-oriented fine-tuning upon the request of users. Specifically, NASOA first adopts an offline NAS to identify a group of training-efficient networks to form a pretrained model zoo. We propose a novel joint block and macro-level search space to enable a flexible and efficient search. Then, by estimating fine-tuning performance via an adaptive model by accumulating experience from the past tasks, an online schedule generator is proposed to pick up the most suitable model and generate a personalized training regime with respect to each desired task in a one-shot fashion. The resulting model zoo is more training efficient than SOTA models, e.g. 6x faster than RegNetY-16GF, and 1.7x faster than EfficientNetB3. Experiments on multiple datasets also show that NASOA achieves much better fine-tuning results, i.e. improving around 2.1% accuracy than the best performance in RegNet series under various constraints and tasks; 40x faster compared to the BOHB.

cs.CV↗