102 research outputs found
A Typha Angustifolia-like MoS2/carbon nanofiber composite for high performance Li-S batteries
A Typha Angustifolia-like MoS2/carbon nanofiber composite as both a chemically trapping agent and redox conversion catalyst for lithium polysulfides has been successfully synthesized via a simple hydrothermal method. Cycling performance and coulombic efficiency have been improved significantly by applying the Typha Angustifolia-like MoS2/carbon nanofiber as the interlayer of a pure sulfur cathode, resulting in a capacity degradation of only 0.09% per cycle and a coulombic efficiency which can reach as high as 99%
FedCut: A Spectral Analysis Framework for Reliable Detection of Byzantine Colluders
This paper proposes a general spectral analysis framework that thwarts a
security risk in federated Learning caused by groups of malicious Byzantine
attackers or colluders, who conspire to upload vicious model updates to
severely debase global model performances. The proposed framework delineates
the strong consistency and temporal coherence between Byzantine colluders'
model updates from a spectral analysis lens, and, formulates the detection of
Byzantine misbehaviours as a community detection problem in weighted graphs.
The modified normalized graph cut is then utilized to discern attackers from
benign participants. Moreover, the Spectral heuristics is adopted to make the
detection robust against various attacks. The proposed Byzantine colluder
resilient method, i.e., FedCut, is guaranteed to converge with bounded errors.
Extensive experimental results under a variety of settings justify the
superiority of FedCut, which demonstrates extremely robust model performance
(MP) under various attacks. It was shown that FedCut's averaged MP is 2.1% to
16.5% better than that of the state of the art Byzantine-resilient methods. In
terms of the worst-case model performance (MP), FedCut is 17.6% to 69.5% better
than these methods
RIDERS: Radar-Infrared Depth Estimation for Robust Sensing
Dense depth recovery is crucial in autonomous driving, serving as a
foundational element for obstacle avoidance, 3D object detection, and local
path planning. Adverse weather conditions, including haze, dust, rain, snow,
and darkness, introduce significant challenges to accurate dense depth
estimation, thereby posing substantial safety risks in autonomous driving.
These challenges are particularly pronounced for traditional depth estimation
methods that rely on short electromagnetic wave sensors, such as visible
spectrum cameras and near-infrared LiDAR, due to their susceptibility to
diffraction noise and occlusion in such environments. To fundamentally overcome
this issue, we present a novel approach for robust metric depth estimation by
fusing a millimeter-wave Radar and a monocular infrared thermal camera, which
are capable of penetrating atmospheric particles and unaffected by lighting
conditions. Our proposed Radar-Infrared fusion method achieves highly accurate
and finely detailed dense depth estimation through three stages, including
monocular depth prediction with global scale alignment, quasi-dense Radar
augmentation by learning Radar-pixels correspondences, and local scale
refinement of dense depth using a scale map learner. Our method achieves
exceptional visual quality and accurate metric estimation by addressing the
challenges of ambiguity and misalignment that arise from directly fusing
multi-modal long-wave features. We evaluate the performance of our approach on
the NTU4DRadLM dataset and our self-collected challenging ZJU-Multispectrum
dataset. Especially noteworthy is the unprecedented robustness demonstrated by
our proposed method in smoky scenarios. Our code will be released at
\url{https://github.com/MMOCKING/RIDERS}.Comment: 13 pages, 13 figure
Interface Engineering via Ti3C2Tx MXene Electrolyte Additive toward Dendrite-Free Zinc Deposition
Zinc metal batteries have been considered as a promising candidate for next-generation batteries due to their high safety and low cost. However, their practical applications are severely hampered by the poor cyclability that caused by the undesired dendrite growth of metallic Zn. Herein, Ti3C2Tx MXene was first used as electrolyte additive to facilitate the uniform Zn deposition by controlling the nucleation and growth process of Zn. Such MXene additives can not only be absorbed on Zn foil to induce uniform initial Zn deposition via providing abundant zincophilic-O groups and subsequently participate in the formation of robust solid-electrolyte interface film, but also accelerate ion transportation by reducing the Zn2+ concentration gradient at the electrode/electrolyte interface. Consequently, MXene-containing electrolyte realizes dendrite-free Zn plating/striping with high Coulombic efficiency (99.7%) and superior reversibility (stably up to 1180 cycles). When applied in full cell, the Zn-V2O5 cell also delivers significantly improved cycling performances. This work provides a facile yet effective method for developing reversible zinc metal batteries
Multi-Core-shell structured LiFePO4@Na3V2(PO4)3@C composite for enhanced low-temperature performance of lithium ion batteries
In this work, a multi-core–shell-structured LiFePO4@Na3V2(PO4)3@C (LFP@NVP@C) composite was successfully designed and prepared to address inferior low-temperature performance of LiFePO4 cathode for lithium-ion batteries. Transmission electron microscopy (TEM) confirms the inner NVP and outer carbon layers co-existed on the surface of LFP particle. When evaluated at low-temperature operation, LFP@NVP@C composite exhibits an evidently enhanced electrochemical performance in term of higher capacity and lower polarization, compared with LFP@C. Even at − 10 °C with 0.5C, LFP@NVP@C delivers a discharge capacity of ca. 96.9 mAh·g−1 and discharge voltage of ca. 3.3 V, which is attributed to the beneficial contribution of NVP coating. NASICON-structured NVP with an open framework for readily insertion/desertion of Li+ will effectively reduce the polarization for the electrochemical reactions of the designed LFP@NVP@C composite
Half-Sphere Shell Supported Pt Catalyst for Electrochemical Methanol Oxidation
Bi-functional effect, elevated mass transport and increased durability have been combined within one catalyst for electrochemical methanol oxidation reaction. It has niobium (Nb) doped titanium dioxides (TiO2) nanosized half-sphere shell (HSS) as the substrate material deposited with small amount of Pt nanoparticles. These specially designed HSS nanostructure has significantly increased surface areas which are suitable for Pt nanoparticles to be deposited onto them to form the catalyst denoted as Pt/Nb-TiO2 HSS. It exhibits a remarkably high methanol oxidation activity of 0.21 V vs. RHE which is 0.05 V lower than HiSPEC10000 PtRu/C catalyst, due to the substrate's strong metal support interactions effect, bi-functional effect and the special structure. These HSS nanostructures have also increased the methanol diffusion and mass transport within the anode to give a maximum power output of 0.0931 W of cathode polarization in miniature direct methanol fuel cell (DMFC). It also acts as protection shells, which minimises the dissolution of Pt metal nanoparticles to prevent its diffusion through the membrane
Electrolyte Salts and Additives Regulation Enables High Performance Aqueous Zinc Ion Batteries: A Mini Review
Aqueous zinc ion batteries (ZIBs) are regarded as one of the most ideally suited candidates for large-scale energy storage applications owning to their obvious advantages, that is, low cost, high safety, high ionic conductivity, abundant raw material resources, and eco-friendliness. Much effort has been devoted to the exploration of cathode materials design, cathode storage mechanisms, anode protection as well as failure mechanisms, while inadequate attentions are paid on the performance enhancement through modifying the electrolyte salts and additives. Herein, to fulfill a comprehensive aqueous ZIBs research database, a range of recently published electrolyte salts and additives research is reviewed and discussed. Furthermore, the remaining challenges and future directions of electrolytes in aqueous ZIBs are also suggested, which can provide insights to push ZIBs’ commercialization
Hardware-algorithm collaborative computing with photonic spiking neuron chip based on integrated Fabry-P\'erot laser with saturable absorber
Photonic neuromorphic computing has emerged as a promising avenue toward
building a low-latency and energy-efficient non-von-Neuman computing system.
Photonic spiking neural network (PSNN) exploits brain-like spatiotemporal
processing to realize high-performance neuromorphic computing. However, the
nonlinear computation of PSNN remains a significant challenging. Here, we
proposed and fabricated a photonic spiking neuron chip based on an integrated
Fabry-P\'erot laser with a saturable absorber (FP-SA) for the first time. The
nonlinear neuron-like dynamics including temporal integration, threshold and
spike generation, refractory period, and cascadability were experimentally
demonstrated, which offers an indispensable fundamental building block to
construct the PSNN hardware. Furthermore, we proposed time-multiplexed spike
encoding to realize functional PSNN far beyond the hardware integration scale
limit. PSNNs with single/cascaded photonic spiking neurons were experimentally
demonstrated to realize hardware-algorithm collaborative computing, showing
capability in performing classification tasks with supervised learning
algorithm, which paves the way for multi-layer PSNN for solving complex tasks.Comment: 10 pages, 8 figure
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
We introduce Generalized Instruction Tuning (called GLAN), a general and
scalable method for instruction tuning of Large Language Models (LLMs). Unlike
prior work that relies on seed examples or existing datasets to construct
instruction tuning data, GLAN exclusively utilizes a pre-curated taxonomy of
human knowledge and capabilities as input and generates large-scale synthetic
instruction data across all disciplines. Specifically, inspired by the
systematic structure in human education system, we build the taxonomy by
decomposing human knowledge and capabilities to various fields, sub-fields and
ultimately, distinct disciplines semi-automatically, facilitated by LLMs.
Subsequently, we generate a comprehensive list of subjects for every discipline
and proceed to design a syllabus tailored to each subject, again utilizing
LLMs. With the fine-grained key concepts detailed in every class session of the
syllabus, we are able to generate diverse instructions with a broad coverage
across the entire spectrum of human knowledge and skills. Extensive experiments
on large language models (e.g., Mistral) demonstrate that GLAN excels in
multiple dimensions from mathematical reasoning, coding, academic exams,
logical reasoning to general instruction following without using task-specific
training data of these tasks. In addition, GLAN allows for easy customization
and new fields or skills can be added by simply incorporating a new node into
our taxonomy.Comment: Work in progres
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