Advanced gel polymer electrolyte for lithium-ion polymer batteries

In order to keep abreast with the rapid development of portable electronic equipment, improving the performance of polymer electrolytes has therefore become our goal of research. This work improved performance of Li-ion polymer batteries through advanced gel polymer electrolytes (GPEs). Comparing with liquid type Electrolyte, Gel type Polymer Electrolyte (GPE) had the advantage of a wide variety of shape, size and dimensions so that GPE was selected as our target. The GPE is a membrane synthetized by trapping ethylene carbonate, and propylene carbonate in polyvinylidene fluoride and 1-Methyl-2-pyrrolidone solutions. Advanced GPEs were synthesized by incorporating an organic electrolyte solution (LiPF6-EC-PC) with ionic liquid (EMI-Tf) into polyvinylidene fluride-base membranes. Among a series of test including ionic conductivity, film resistance, cell voltage, cyclic voltammetry, and charge/discharge efficiency, 50 volume percent of ionic liquid (EMI-Tf) in an organic electrolyte solution showed the best performance. We also introduced the nanoparticle-polymer techniques that gold nanoparticles were adding to the GPE membranes as the fillers in order to higher capacity, stronger mechanical strength, and lower internal resistances

REMARK-LLM: A Robust and Efficient Watermarking Framework for Generative Large Language Models

We present REMARK-LLM, a novel efficient, and robust watermarking framework designed for texts generated by large language models (LLMs). Synthesizing human-like content using LLMs necessitates vast computational resources and extensive datasets, encapsulating critical intellectual property (IP). However, the generated content is prone to malicious exploitation, including spamming and plagiarism. To address the challenges, REMARK-LLM proposes three new components: (i) a learning-based message encoding module to infuse binary signatures into LLM-generated texts; (ii) a reparameterization module to transform the dense distributions from the message encoding to the sparse distribution of the watermarked textual tokens; (iii) a decoding module dedicated for signature extraction; Furthermore, we introduce an optimized beam search algorithm to guarantee the coherence and consistency of the generated content. REMARK-LLM is rigorously trained to encourage the preservation of semantic integrity in watermarked content, while ensuring effective watermark retrieval. Extensive evaluations on multiple unseen datasets highlight REMARK-LLM proficiency and transferability in inserting 2 times more signature bits into the same texts when compared to prior art, all while maintaining semantic integrity. Furthermore, REMARK-LLM exhibits better resilience against a spectrum of watermark detection and removal attacks

XVTP3D: Cross-view Trajectory Prediction Using Shared 3D Queries for Autonomous Driving

Trajectory prediction with uncertainty is a critical and challenging task for autonomous driving. Nowadays, we can easily access sensor data represented in multiple views. However, cross-view consistency has not been evaluated by the existing models, which might lead to divergences between the multimodal predictions from different views. It is not practical and effective when the network does not comprehend the 3D scene, which could cause the downstream module in a dilemma. Instead, we predicts multimodal trajectories while maintaining cross-view consistency. We presented a cross-view trajectory prediction method using shared 3D Queries (XVTP3D). We employ a set of 3D queries shared across views to generate multi-goals that are cross-view consistent. We also proposed a random mask method and coarse-to-fine cross-attention to capture robust cross-view features. As far as we know, this is the first work that introduces the outstanding top-down paradigm in BEV detection field to a trajectory prediction problem. The results of experiments on two publicly available datasets show that XVTP3D achieved state-of-the-art performance with consistent cross-view predictions.Comment: 11 pages, 6 figures, accepted by IJCAI 2

Shadow fading cross-correlation of multi-frequencies in curved subway tunnels

Radio propagation characteristics in curved tunnels are important for designing reliable communications in subway systems. In this paper, shadow fading is characterized, and cross-correlation property of shadow fading for different frequency bands is investigated based on empirical measurements. The measurements were conducted in two types of curved subway tunnels with 300 m and 500 m radii of curvatures at 980 MHz, 2400 MHz, and 5705 MHz, respectively. The impact of antenna polarization and propagation environment on shadow fading correlation at the receiver is evaluated. It is found that shadow fading with horizontal polarized antenna exhibits less correlation than with vertical polarized antenna. Strong independence of shadowing correlation and tunnel type is observed. Furthermore, a heuristic explanation of the particular shadowing correlation property in subway tunnel is presented

Robust Mixture-of-Expert Training for Convolutional Neural Networks

Sparsely-gated Mixture of Expert (MoE), an emerging deep model architecture, has demonstrated a great promise to enable high-accuracy and ultra-efficient model inference. Despite the growing popularity of MoE, little work investigated its potential to advance convolutional neural networks (CNNs), especially in the plane of adversarial robustness. Since the lack of robustness has become one of the main hurdles for CNNs, in this paper we ask: How to adversarially robustify a CNN-based MoE model? Can we robustly train it like an ordinary CNN model? Our pilot study shows that the conventional adversarial training (AT) mechanism (developed for vanilla CNNs) no longer remains effective to robustify an MoE-CNN. To better understand this phenomenon, we dissect the robustness of an MoE-CNN into two dimensions: Robustness of routers (i.e., gating functions to select data-specific experts) and robustness of experts (i.e., the router-guided pathways defined by the subnetworks of the backbone CNN). Our analyses show that routers and experts are hard to adapt to each other in the vanilla AT. Thus, we propose a new router-expert alternating Adversarial training framework for MoE, termed AdvMoE. The effectiveness of our proposal is justified across 4 commonly-used CNN model architectures over 4 benchmark datasets. We find that AdvMoE achieves 1% ~ 4% adversarial robustness improvement over the original dense CNN, and enjoys the efficiency merit of sparsity-gated MoE, leading to more than 50% inference cost reduction. Codes are available at https://github.com/OPTML-Group/Robust-MoE-CNN.Comment: ICCV 202

Measurement and analysis of extra propagation loss of tunnel curve

Wave propagation experiences extra loss in curved tunnels, which is highly desired for network planning. Extensive narrow-band propagation measurements are made in two types of Madrid subway tunnels (different cross sections and curvatures) with various configurations (different frequencies and polarizations). A ray tracer validated by the straight and curved parts of the measuring tunnels is employed to simulate the reference received signal power by assuming the curved tunnel to be straight. By subtracting the measured received power in the curved tunnels from the simulated reference power, the extra loss resulting from the tunnel curve is extracted. Finally, this paper presents the figures and tables quantitatively reflecting the correlations between the extra loss and radius of curvature, frequency, polarization, and cross section, respectively. The results are valuable for statistical modeling and the involvement of the extra loss in the design and network planning of communication systems in subway tunnels

Measurements and analysis of large-scale fading characteristics in curved subway tunnels at 920 MHz, 2400 MHz, and 5705 MHz

ave propagation characteristics in curved tunnels are of importance for designing reliable communications in subway systems. This paper presents the extensive propagation measurements conducted in two typical types of subway tunnels—traditional arched “Type I” tunnel and modern arched “Type II” tunnel—with300- and 500-m radii of curvature with different configurations—horizontal and vertical polarizations at 920, 2400, and 5705 MHz, respectively. Based on the measurements, statistical metrics of propagation loss and shadow fading (path-loss exponent, shadow fading distribution, autocorrelation, and cross-correlation) in all the measurement cases are extracted. Then, the large-scale fading characteristics in the curved subway tunnels are compared with the cases of road and railway tunnels, the other main rail traffic scenarios, and some “typical” scenarios to give a comprehensive insight into the propagation in various scenarios where the intelligent transportation systems are deployed. Moreover, for each of the large-scale fading parameters, extensive analysis and discussions are made to reflect the physical laws behind the observations. The quantitative results and findings are useful to realize intelligent transportation systems in the subway system

Emotional Voice Puppetry

The paper presents emotional voice puppetry, an audio-based facial animation approach to portray characters with vivid emotional changes. The lips motion and the surrounding facial areas are controlled by the contents of the audio, and the facial dynamics are established by category of the emotion and the intensity. Our approach is exclusive because it takes account of perceptual validity and geometry instead of pure geometric processes. Another highlight of our approach is the generalizability to multiple characters. The findings showed that training new secondary characters when the rig parameters are categorized as eye, eyebrows, nose, mouth, and signature wrinkles is significant in achieving better generalization results compared to joint training. User studies demonstrate the effectiveness of our approach both qualitatively and quantitatively. Our approach can be applicable in AR/VR and 3DUI, namely, virtual reality avatars/self-avatars, teleconferencing and in-game dialogue

Reconstructing Ocean-Plate Stratigraphy (OPS) to Understand Accretionary Style and Mélange Fabric:Insights From the Bangong-Nujiang Suture (Tibet, China)

Ocean-plate stratigraphy (OPS) refers to the lithostratigraphic column atop an ocean plate, which becomes scraped off during subduction and preserved in accretionary complex (AC). Herein, based on structural, stratigraphic, and geochronological studies of ACs from the Bangong-Nujiang suture, we demonstrate that OPS can facilitate interpreting structural and compositional heterogeneities in ACs. Carefully correlated OPSs reveal that, on the overall sediment-rich lower plate, different types of basement topography correspond to the accretion of distinct litho-structural assemblages. In particular, subduction of the major, high-relief Zhonggang seamount eroded the earlier margin and was subsequently accreted as coherent seamount slices. In contrast, subduction of the lower-relief, Gaize seamount halted frontal accretion of trailing sediments, which were dragged downward to the seismogenic depth and underplated as pervasive, shear-related broken formations. Such broken formations may fingerprint past lower-relief-seamount subduction in other fossil ACs