Nodal topological superconductivity in nodal-line semimetals

We analyze possible nodal superconducting phases that emerge from a doped nodal-line semimetal. We show that nodal-line superconducting phases are favored by interactions mediated by short-range ferromagnetic fluctuations or Hund's coupling. It is found that the leading pairing channels are momentum-independent, orbital-singlet and spin-triplet. In the pairing state, we show that the Bogoliubov-de Gennes (BdG) Hamiltonian hosts a pair of topologically protected nodal rings on the equators of the torus Fermi surface (FS). Using a topological classification for gapless systems with inversion symmetry, we find that these nodal rings are topologically nontrivial and protected by integer-valued monopole charges $\nu = \pm 2$. In the scenario of pairing driven by ferromagnetic fluctuations, we analyze the fate of superconductivity in the magnetically ordered phase. Based on Ginzburg-Landau free energy analysis, we find the energetically favored superconducting state is characterized by the coexistence of two pairing orders whose $\bf d$-vectors are perpendicular to the magnetization axis $\bf M$ with their phases unfixed. In this case, each nodal loop in the pairing state splits into two, carrying a $\pm 1$ monopole charge. For bulk-boundary correspondence, these nodal rings enclose flat-band Majorana zero modes on top and bottom surface Brillouin Zones with distinct $\mathbb{Z}$-valued topological invariants.Comment: 16 pages, 10 figure

An Improved Antenna Group Delay Measurement Method Using a Three-antenna Extrapolation Technique

In order to minimize the error due to multiple reflections between antennas in the conventional group delay (GD) measurement, an improved antenna GD measurement method is proposed. In this method, antenna group delay is measured as a function of distances using a three-antenna extrapolation method. The GD is determined by averaging a set of measured GD values according to a derived multiple-reflection error model. Measurement in frequency band of (1575.42±16) MHz for a circularly polarised helical antenna is presented, which gives the detail measurement procedures and validates the method. The uncertainty evaluation for this measurement was carried out as well, and an expanded uncertainty of 0.20 ns (k = 2) has been achieved. One more measurement example in frequency band of (4000±10) MHz for a standard gain horn antenna with an expanded uncertainty of 0.12 ns (k = 2) is also presented briefly in this paper

Octavius: Mitigating Task Interference in MLLMs via LoRA-MoE

Recent studies have demonstrated Large Language Models (LLMs) can extend their zero-shot generalization capabilities to multimodal learning through instruction tuning. As more modalities and downstream tasks are introduced, negative conflicts and interference may have a worse impact on performance. While this phenomenon has been overlooked in previous work, we propose a novel and extensible framework, called Octavius, for comprehensive studies and experimentation on multimodal learning with Multimodal Large Language Models (MLLMs). Specifically, we combine the well-known Mixture-of-Experts (MoE) and one of the representative PEFT techniques, i.e., LoRA, designing a novel LLM-based decoder, called LoRA-MoE, for multimodal learning. To the best of our knowledge, we are one of the pioneering efforts to introduce MoE into MLLMs to address this problem. The experimental results (about 20% improvement) have shown the effectiveness and versatility of our design in various 2D and 3D downstream tasks. Code and datasets are available at https://openlamm.github.io/paper_list/Octavius.Comment: 22 pages, 12 figures. Accepted in ICLR 202

Hybrid Dual Attack on LWE with Arbitrary Secrets

In this paper, we study the {\em hybrid dual attack} over Learning with Errors (LWE) problems for {\em any} secret distribution. Prior to our work, hybrid attacks are only considered for sparse and/or small secrets. A new and interesting result from our analysis shows that for most cryptographic use cases a hybrid dual attack outperforms a standalone dual attack, regardless of the secret distribution. We formulate our results into a framework of predicting the performance of the hybrid dual attacks. We also present a few tricks that further improve our attack. To illustrate the effectiveness of our result, we re-evaluate the security of {\em all} LWE related proposals in round 3 of NIST\u27s post-quantum cryptography process, and improve the state-of-the-art cryptanalysis results by 2-14 bits, under the BKZ-core-SVP model

Stator Flux Observer for Induction Motor Based on Tracking Differentiator

Voltage model is commonly used in direct torque control (DTC) for flux observing of asynchronous motor. In order to improve low-speed and dynamic performance of the voltage model, a modified low-pass filter (LPF) algorithm is proposed. Firstly, the tracking differentiator is brought in to modulate the measured stator current, which suppresses the measurement noise, and then amplitude and phase compensation is made towards the stator electromotive force (EMF), after which the stator flux is obtained through a low-pass filter. This method can eliminate the dynamic error of flux filtered by LPF and improve low-speed performance. Experimental results demonstrate effectiveness and improved dynamic performance of such method

LAMM: Language-Assisted Multi-Modal Instruction-Tuning Dataset, Framework, and Benchmark

Large language models have become a potential pathway toward achieving artificial general intelligence. Recent works on multi-modal large language models have demonstrated their effectiveness in handling visual modalities. In this work, we extend the research of MLLMs to point clouds and present the LAMM-Dataset and LAMM-Benchmark for 2D image and 3D point cloud understanding. We also establish an extensible framework to facilitate the extension of MLLMs to additional modalities. Our main contribution is three-fold: 1) We present the LAMM-Dataset and LAMM-Benchmark, which cover almost all high-level vision tasks for 2D and 3D vision. Extensive experiments validate the effectiveness of our dataset and benchmark. 2) We demonstrate the detailed methods of constructing instruction-tuning datasets and benchmarks for MLLMs, which will enable future research on MLLMs to scale up and extend to other domains, tasks, and modalities faster. 3) We provide a primary but potential MLLM training framework optimized for modalities' extension. We also provide baseline models, comprehensive experimental observations, and analysis to accelerate future research. Codes and datasets are now available at https://github.com/OpenLAMM/LAMM.Comment: 37 pages, 33 figures. Code available at https://github.com/OpenLAMM/LAMM ; Project page: https://openlamm.github.io

Artificial Intelligence-Based Classification of CT Images Using a Hybrid SpinalZFNet

The kidney is an abdominal organ in the human body that supports filtering excess water and waste from the blood. Kidney diseases generally occur due to changes in certain supplements, medical conditions, obesity, and diet, which causes kidney function and ultimately leads to complications such as chronic kidney disease, kidney failure, and other renal disorders. Combining patient metadata with computed tomography (CT) images is essential to accurately and timely diagnosing such complications. Deep Neural Networks (DNNs) have transformed medical fields by providing high accuracy in complex tasks. However, the high computational cost of these models is a significant challenge, particularly in real-time applications. This paper proposed SpinalZFNet, a hybrid deep learning approach that integrates the architectural strengths of Spinal Network (SpinalNet) with the feature extraction capabilities of Zeiler and Fergus Network (ZFNet) to classify kidney disease accurately using CT images. This unique combination enhanced feature analysis, significantly improving classification accuracy while reducing the computational overhead. At first, the acquired CT images are pre-processed using a median filter, and the pre-processed image is segmented using Efficient Neural Network (ENet). Later, the images are augmented, and different features are extracted from the augmented CT images. The extracted features finally classify the kidney disease into normal, tumor, cyst, and stone using the proposed SpinalZFNet model. The SpinalZFNet outperformed other models, with 99.9% sensitivity, 99.5% specificity, precision 99.6%, 99.8% accuracy, and 99.7% F1-Score in classifying kidney disease

The GATA factor HANABA TARANU promotes runner formation by regulating axillary bud initiation and outgrowth in cultivated strawberry

A runner, as an elongated branch, develops from the axillary bud (AXB) in the leaf axil and is crucial for the clonal propagation of cultivated strawberry (Fragaria x ananassa Duch.). Runner formation occurs in at least two steps: AXB initiation and AXB outgrowth. HANABA TARANU (HAN ) encodes a GATA transcription factor that affects AXB initiation in Arabidopsis and promotes branching in grass species, but the underlying mechanism is largely unknown. Here, the function of a strawberry HAN homolog FaHAN in runner formation was characterized. FaHAN transcripts can be detected in the leaf axils. Overexpression (OE) of FaHAN increased the number of runners, mainly by enhancing AXB outgrowth, in strawberry. The expression of the strawberry homolog of BRANCHED1 , a key inhibitor of AXB outgrowth in many plant species, was significantly downregulated in the AXBs of FaHAN -OE lines, whereas the expression of the strawberry homolog of SHOOT MERISTEMLESS, a marker gene for AXB initiation in Arabidopsis, was upregulated. Moreover, several genes of gibberellin biosynthesis and cytokinin signaling pathways were activated, whereas the auxin response pathway genes were repressed. Further assays indicated that FaHAN could be directly activated by FaNAC2, the overexpression of which in strawberry also increased the number of runners. The silencing of FaNAC2 or FaHAN inhibited AXB initiation and led to a higher proportion of dormant AXBs, confirming their roles in the control of runner formation. Taken together, our results revealed a FaNAC2-FaHAN pathway in the control of runner formation and have provided a means to enhance the vegetative propagation of cultivated strawberry.Peer reviewe