Search

Metamaterials have unlocked unprecedented control over light by leveraging novel mechanisms to expand their functionality. Non-Hermitian physics further enhances the tunability of non-Hermitian metamaterials (NHMs) through phenomena such as the non-Hermitian skin effect (NHSE), enabling applications like directional amplification. The higher-dimensional NHSE manifests unique effects, including the algebraic skin effect (ASE), which features power-law decay instead of exponential localization, allowing for quasi-long-range interactions. In this work, we establish apparent criteria for achieving ASE in two-dimensional reciprocal NHMs with anisotropic and complex dielectric tensors. By numerically and theoretically demonstrating ASE through mismatched optical axes and geometric structures, we reveal that ASE is governed by a generalized Fermi surface whose dimensionality exceeds that of the Fermi surface. We further propose and validate a realistic photonic crystal design for ASE, which is experimentally accessible. Our recipe for ASE provides a versatile pathway for broader generalizations, including three-dimensional structures, synthetic dimensions, and other classical wave systems, paving the way for advancements in non-Hermitian photonics., Comment: 23 pages, 4 figures

In this paper, we introduce Motion-X++, a large-scale multimodal 3D expressive whole-body human motion dataset. Existing motion datasets predominantly capture body-only poses, lacking facial expressions, hand gestures, and fine-grained pose descriptions, and are typically limited to lab settings with manually labeled text descriptions, thereby restricting their scalability. To address this issue, we develop a scalable annotation pipeline that can automatically capture 3D whole-body human motion and comprehensive textural labels from RGB videos and build the Motion-X dataset comprising 81.1K text-motion pairs. Furthermore, we extend Motion-X into Motion-X++ by improving the annotation pipeline, introducing more data modalities, and scaling up the data quantities. Motion-X++ provides 19.5M 3D whole-body pose annotations covering 120.5K motion sequences from massive scenes, 80.8K RGB videos, 45.3K audios, 19.5M frame-level whole-body pose descriptions, and 120.5K sequence-level semantic labels. Comprehensive experiments validate the accuracy of our annotation pipeline and highlight Motion-X++'s significant benefits for generating expressive, precise, and natural motion with paired multimodal labels supporting several downstream tasks, including text-driven whole-body motion generation,audio-driven motion generation, 3D whole-body human mesh recovery, and 2D whole-body keypoints estimation, etc., Comment: 17 pages, 14 figures, This work extends and enhances the research published in the NeurIPS 2023 paper, "Motion-X: A Large-scale 3D Expressive Whole-body Human Motion Dataset". arXiv admin note: substantial text overlap with arXiv:2307.00818

Topological Anderson insulators represent a class of disorder-induced, nontrivial topological states. In this study, we propose a feasible strategy to unveil and design the latent-symmetry protected topological Anderson insulators. By employing the isospectral reduction approach from graph theory, we reduce a family of the disordered multi-atomic chains to the disordered dimerized chain characterized by energy-dependent potentials and hoppings, which exhibits the chiral symmetry or inversion symmetry. According to the topological invariants, bulk polarization, and the divergence of localization length of the topological bound edge states in the reduced disordered system, the gapped and ungapped topological Anderson states with latent symmetry could be identified in the original disordered multi-atomic systems. The concept of topological Anderson insulating phases protected by the geometric symmetries and tenfold-way classification is thus extended to the various types of latent symmetry cases. This work paves the way for exploiting topological Anderson insulators in terms of latent symmetries., Comment: 9 pages, 5 figures, Comments are welcome

The hyperbolic lattice (HBL) has emerged as a compelling platform for exploring matter in non-Euclidean space. Among its notable features, the breakdown of the conventional Bloch theorem stands out, prompting a reexamination of band theory, with the determination of spectra for non-Hermitian systems being a prominent example. Here, we develop an approach to determining the spectra under open boundary conditions (OBCs), one of the foundations in non-Hermitian lattices, from the reciprocal space of HBLs. By introducing supercells to encompass states that are allowed by non-Abelian translational groups, we perform analytic continuation and base on the point gap topology to acquire uniform spectra, the universal OBC spectral range. Applying this method to a single-band nonreciprocal model and a reciprocal non-Abelian semimetal model, we reveal higher-dimensional skin effects and topological phase transitions, respectively, demonstrating the feasibility of our method in predicting spectral topology and investigating non-Hermitian physics in HBLs., Comment: 19 pages, 3 figures

Fungal keratitis is a severe vision-threatening corneal infection with a prognosis influenced by fungal virulence and the host's immune defense mechanisms. The immune system, through its regulation of the inflammatory response, ensures cells and tissues can effectively activate defense mechanisms in response to infection and injury. However, there is still a lack of effective drugs that attenuate fungal virulence while relieving the inflammatory response caused by fungal keratitis. Therefore, finding effective treatments to solve these problems is particularly important. We synthesized ZIF-90 by water-based synthesis and characterized by SEM, XRD etc. In vitro experiments included CCK-8 and ELISA. These evaluations verified the disruptive effects of ZIF-90 on Aspergillus. fumigatus spore adhesion, morphology, cell membrane, and the effect of ZIF-90 on apoptosis. In addition, to investigate whether the metal-ligand zinc and the organic ligand imidazole act as essential factors in ZIF-90, we investigated the in vitro antimicrobial and anti-inflammatory effects of ZIF-8, ZIF-67, and MOF-74 (Zn) by MIC and ELISA experiments. ZIF-90 has therapeutic effects on fungal keratitis, which could break the protective organelles of Aspergillus. fumigatus, such as the cell wall. In addition, ZIF-90 can avoid excessive inflammatory response by promoting apoptosis of inflammatory cells. The results demonstrated that both zinc ions and imidazole possessed antimicrobial and anti-inflammatory effects. In addition, ZIF-90 exhibited better biocompatibility compared to ZIF-8, ZIF-67, and MOF-74 (Zn). ZIF-90 has anti-inflammatory and antifungal effects and preferable biocompatibility, and has great potential for the treatment of fungal keratitis.

Lighting asset management in public libraries has traditionally been reactive, focusing on corrective maintenance, addressing issues only when failures occur. Although standards now encourage preventive measures, such as incorporating a maintenance factor, the broader goal of human centric, sustainable lighting systems requires a shift toward predictive maintenance strategies. This study introduces an enhanced digital twin model designed for the proactive management of lighting assets in public libraries. By integrating descriptive, diagnostic, predictive, and prescriptive analytics, the model enables a comprehensive, multilevel view of asset health. The proposed framework supports both preventive and predictive maintenance strategies, allowing for early detection of issues and the timely resolution of potential failures. In addition to the specific application for lighting systems, the design is adaptable for other building assets, providing a scalable solution for integrated asset management in various public spaces.

Background: Fungal keratitis is a serious blinding eye disease. Traditional drugs used to treat fungal keratitis commonly have the disadvantages of low bioavailability, poor dispersion, and limited permeability. Purpose: To develop a new method for the treatment of fungal keratitis with improved bioavailability, dispersion, and permeability. Purpose: To develop a new method for the treatment of fungal keratitis with improved bioavailability, dispersion, and permeability. Methods: Zeolitic Imidazolate Framework-8 (ZIF-8) was formed by zinc ions and 2-methylimidazole linked by coordination bonds and characterized by Scanning electron microscopy (SEM), X-ray diffraction (XRD), and Zeta potential. The safety of ZIF-8 on HCECs and RAW 264.7 cells was detected by Cell Counting Kit-8 (CCK-8). The anti-inflammatory effects of ZIF-8 on RAW 246.7 cells were evaluated by Quantitative Real-Time PCR Experiments (qPCR) and Enzyme-linked immunosorbent assay (ELISA). Clinical score, Colony-Forming Units (CFU). In vivo, treatment with ZIF-8 reduced corneal fungal load and mitigated neutrophil infiltration in fungal keratitis, which effectively reduced the severity of keratitis in mice and alleviated the infiltration of inflammatory factors in the mouse cornea. In addition, ZIF-8 reduces the inflammatory response by downregulating the expression of pro-inflammatory cytokines TNF-{\alpha}, IL-6, and IL-1\b{eta} after Aspergillus fumigatus infection in vivo and in vitro. Conclusion: ZIF-8 has a significant anti-inflammatory and antifungal effect, which provides a new solution for the treatment of fungal keratitis., Comment: 25 pages, 8 figures, this paper has been received by international journal of nanomedicine

A different type of topological phase dubbed topological inverse Anderson insulators is proposed, which is characterized by the disorder-induced extended bulk states from the flat-band localization and topological edge states. Based on the topological invariant, the behaviors of the localization length of the zero-energy modes, and quantum transport, we identify its existence in several all-band-flat models with the disordered potentials or hopping including the $\pi$-flux Creutz ladder, the fully dimerized Su-Schrieffer-Heeger chain, and $\pi$-flux diamond chain. Unlike the topological Anderson insulator, where disorder induces localization and exponential suppression of transport, the disorder-assisted quantum ballistic coherent transport can appear in the topological inverse Anderson insulator. In addition, our proposal and results could be realized by the current experimental techniques., Comment: 9 pages, 5 figures

Mixers play a crucial role in superconducting quantum computing, primarily by facilitating frequency conversion of signals to enable precise control and readout of quantum states. However, imperfections, particularly carrier leakage and unwanted sideband signal, can significantly compromise control fidelity. To mitigate these defects, regular and precise mixer calibrations are indispensable, yet they pose a formidable challenge in large-scale quantum control. Here, we introduce an in situ calibration technique and outcome-focused mixer calibration scheme using superconducting qubits. Our method leverages the qubit's response to imperfect signals, allowing for calibration without modifying the wiring configuration. We experimentally validate the efficacy of this technique by benchmarking single-qubit gate fidelity and qubit coherence time., Comment: 9 pages, 7 figures

Mastering basketball skills such as diverse layups and dribbling involves complex interactions with the ball and requires real-time adjustments. Traditional reinforcement learning methods for interaction skills rely on labor-intensive, manually designed rewards that do not generalize well across different skills. Inspired by how humans learn from demonstrations, we propose SkillMimic, a data-driven approach that mimics both human and ball motions to learn a wide variety of basketball skills. SkillMimic employs a unified configuration to learn diverse skills from human-ball motion datasets, with skill diversity and generalization improving as the dataset grows. This approach allows training a single policy to learn multiple skills, enabling smooth skill switching even if these switches are not present in the reference dataset. The skills acquired by SkillMimic can be easily reused by a high-level controller to accomplish complex basketball tasks. To evaluate our approach, we introduce two basketball datasets: one estimated through monocular RGB videos and the other using advanced motion capture equipment, collectively containing about 35 minutes of diverse basketball skills. Experiments show that our method can effectively learn various basketball skills included in the dataset with a unified configuration, including various styles of dribbling, layups, and shooting. Furthermore, by training a high-level controller to reuse the acquired skills, we can achieve complex basketball tasks such as layup scoring, which involves dribbling toward the basket, timing the dribble and layup to score, retrieving the rebound, and repeating the process. The project page and video demonstrations are available at https://ingrid789.github.io/SkillMimic/

The extraction of Metal-Organic Frameworks (MOFs) synthesis conditions from literature text has been challenging but crucial for the logical design of new MOFs with desirable functionality. The recent advent of large language models (LLMs) provides disruptively new solution to this long-standing problem and latest researches have reported over 90% F1 in extracting correct conditions from MOFs literature. We argue in this paper that most existing synthesis extraction practices with LLMs stay with the primitive zero-shot learning, which could lead to downgraded extraction and application performance due to the lack of specialized knowledge. This work pioneers and optimizes the few-shot in-context learning paradigm for LLM extraction of material synthesis conditions. First, we propose a human-AI joint data curation process to secure high-quality ground-truth demonstrations for few-shot learning. Second, we apply a BM25 algorithm based on the retrieval-augmented generation (RAG) technique to adaptively select few-shot demonstrations for each MOF's extraction. Over a dataset randomly sampled from 84,898 well-defined MOFs, the proposed few-shot method achieves much higher average F1 performance (0.93 vs. 0.81, +14.8%) than the native zero-shot LLM using the same GPT-4 model, under fully automatic evaluation that are more objective than the previous human evaluation. The proposed method is further validated through real-world material experiments: compared with the baseline zero-shot LLM, the proposed few-shot approach increases the MOFs structural inference performance (R^2) by 29.4% in average.

The memory and computational demands of Key-Value (KV) cache present significant challenges for deploying long-context language models. Previous approaches attempt to mitigate this issue by selectively dropping tokens, which irreversibly erases critical information that might be needed for future queries. In this paper, we propose a novel compression technique for KV cache that preserves all token information. Our investigation reveals that: i) Most attention heads primarily focus on the local context; ii) Only a few heads, denoted as retrieval heads, can essentially pay attention to all input tokens. These key observations motivate us to use separate caching strategy for attention heads. Therefore, we propose RazorAttention, a training-free KV cache compression algorithm, which maintains a full cache for these crucial retrieval heads and discards the remote tokens in non-retrieval heads. Furthermore, we introduce a novel mechanism involving a "compensation token" to further recover the information in the dropped tokens. Extensive evaluations across a diverse set of large language models (LLMs) demonstrate that RazorAttention achieves a reduction in KV cache size by over 70% without noticeable impacts on performance. Additionally, RazorAttention is compatible with FlashAttention, rendering it an efficient and plug-and-play solution that enhances LLM inference efficiency without overhead or retraining of the original model.

Automated scraping stands out as a common method for collecting data in deep learning models without the authorization of data owners. Recent studies have begun to tackle the privacy concerns associated with this data collection method. Notable approaches include Deepconfuse, error-minimizing, error-maximizing (also known as adversarial poisoning), Neural Tangent Generalization Attack, synthetic, autoregressive, One-Pixel Shortcut, Self-Ensemble Protection, Entangled Features, Robust Error-Minimizing, Hypocritical, and TensorClog. The data generated by those approaches, called "unlearnable" examples, are prevented "learning" by deep learning models. In this research, we investigate and devise an effective nonlinear transformation framework and conduct extensive experiments to demonstrate that a deep neural network can effectively learn from the data/examples traditionally considered unlearnable produced by the above twelve approaches. The resulting approach improves the ability to break unlearnable data compared to the linear separable technique recently proposed by researchers. Specifically, our extensive experiments show that the improvement ranges from 0.34% to 249.59% for the unlearnable CIFAR10 datasets generated by those twelve data protection approaches, except for One-Pixel Shortcut. Moreover, the proposed framework achieves over 100% improvement of test accuracy for Autoregressive and REM approaches compared to the linear separable technique. Our findings suggest that these approaches are inadequate in preventing unauthorized uses of data in machine learning models. There is an urgent need to develop more robust protection mechanisms that effectively thwart an attacker from accessing data without proper authorization from the owners.

Numerous methods have been proposed to detect, estimate, and analyze properties of people in images, including the estimation of 3D pose, shape, contact, human-object interaction, emotion, and more. Each of these methods works in isolation instead of synergistically. Here we address this problem and build a language-driven human understanding system -- ChatHuman, which combines and integrates the skills of many different methods. To do so, we finetune a Large Language Model (LLM) to select and use a wide variety of existing tools in response to user inputs. In doing so, ChatHuman is able to combine information from multiple tools to solve problems more accurately than the individual tools themselves and to leverage tool output to improve its ability to reason about humans. The novel features of ChatHuman include leveraging academic publications to guide the application of 3D human-related tools, employing a retrieval-augmented generation model to generate in-context-learning examples for handling new tools, and discriminating and integrating tool results to enhance 3D human understanding. Our experiments show that ChatHuman outperforms existing models in both tool selection accuracy and performance across multiple 3D human-related tasks. ChatHuman is a step towards consolidating diverse methods for human analysis into a single, powerful, system for 3D human reasoning., Comment: Project page: https://chathuman.github.io

This paper proposes the context driven Critical Integrative Levels (CIL), a novel approach to lighting asset management in public libraries that aligns with the transformative vision of human-centric and integrative lighting. This approach encompasses not only the visual aspects of lighting performance but also prioritizes the physiological and psychological well-being of library users. Incorporating a newly defined metric, Mean Time of Exposure (MTOE), the approach quantifies user-light interaction, enabling tailored lighting strategies that respond to diverse activities and needs in library spaces. Case studies demonstrate how the CIL matrix can be practically applied, offering significant improvements over conventional methods by focusing on optimized user experiences from both visual impacts and non-visual effects.

This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective network design or solution capable of generating brighter, clearer, and visually appealing results when dealing with a variety of conditions, including ultra-high resolution (4K and beyond), non-uniform illumination, backlighting, extreme darkness, and night scenes. A notable total of 428 participants registered for the challenge, with 22 teams ultimately making valid submissions. This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field., Comment: NTIRE 2024 Challenge Report

The topological states of the two-leg and three-leg ladders formed by two trivial quantum wires with different lattice constants are theoretically investigated. Firstly, we take two trivial quantum wires with a lattice constant ratio of 1:2 as an example. For the symmetric nearest-neighbor intra-chain hopping two-leg ladder, the inversion symmetry protected topological insulator phase with two degenerate topological edge states appears. When the inversion symmetry is broken, the topological insulators with one or two topological edge states of different energies and topological metals with edge states embedded in the bulk states could emerge depending on the filling factor. The topological origin of these topological states in the two-leg ladders is the topological properties of the Chern insulators and Chern metals. According to the arrangement of two trivial quantum wires, we construct two types of three-leg ladders. Each type of the three-leg ladder could be divided into one trivial subspace and one topological nontrivial subspace by unitary transformation. The topological nontrivial subspace corresponds to the effective two-leg ladder model. As the filling factor changes, the system could be in topological insulators or topological metals phases. When the two-leg ladder is constructed by two trivial quantum wires with a lattice constant ratio of 1:3 and 2:3, the system could also realize rich topological states such as the topological insulators and topological metals with the topological edge states. These rich topological states in the two-leg and three-leg ladders could be confirmed by current experimental techniques., Comment: 11 pages, 8 figures

In the present work, the cluster radioactivity preformation probability Pc in the scheme of NpNn for the effective number of the valence particles (holes) in trans-lead nuclei has been systematically investigated. This quantity has been explored in the simplified parametrization of NpNn as well as the multiplication NpNnI of this product with the isospin asymmetry I. The calculations for Pc are both performed in microscopic and model-dependent way. Within the microscopic approach, based on our previous work [Chin. Phys. C 47,014101 (2023)], Pc is calculated in cluster formation model (CFM) combined with the exponential relationship of Pc to the alpha decay preformation probability P alpha when the mass number of the emitted cluster Ac less than 28. While Ac greater than 28, Pc is obtained through the charge-number dependence of Pc on the decay products proposed by Ren et al. [Phys. Rev. C 70,034304 (2004)]. In the model-dependent approach, Pc is extracted through the ratios from calculated cluster radioactivity half-lives in the framework of unified fission model (UFM) proposed by Dong et al. [Eur. Phys. J. A 41,197 (2009)] to experimental ones. Both of the results show Pc in logarithmic form are linear to NpNn as well as NpNnI. For comparison, the parent-mass-number dependence analytical formula as well as the model proposed by K. Wei and H. F. Zhang [Phys. Rev. C 96,021601(R)(2017)] are also used. Furthermore, the preformation mechanic for cluster radioactivity has also been discussed.

In the present study, proton emission half-lives have been investigated for the deformed proton emitters with $53\leq Z \leq 83$ in the deformed Gamow-like model, where the deformation effect has been included in the Coulomb potential. The experimental half-lives of proton emitters can be reproduced within a factor of 3.45. For comparison, other results from the universal decay law and the new Geiger-Nuttall law are presented as well. Furthermore, the relevance of the half-lives to the angular momentum $l$ for $^{117}$La, $^{121}$Pr, $^{135}$Tb and $^{141}$Ho has been analyzed, and corresponding possible values of $l$ has been put forward: $l=$3, 3, 4, 4.

In the present work, we systematically study the spectroscopic factor of proton radioactivity ($S_p$) with $A>100$ using the deformed two-potential approach (D-TPA). It is found that there is a link between the quadrupole deformation parameter of proton emitter and $S_p$. Based on this result, we propose a simple analytic formula for estimating the spectroscopic factor of proton radioactivity. With the help of this formula, the calculated half-lives of proton radioactivity can reproduce the experimental data successfully within a factor of 2.77. Furthermore, we extend the D-TPA with this formula for evaluating the spectroscopic factor to predict the proton radioactivity half-lives of 12 proton radioactivity candidates whose radioactivity is energetically allowed or observed but not yet quantified in NUBASE2020. For comparison, the universal decay law for proton radioactivity (UDLP) and the new Geiger-Nuttall law (NG-N) are also used. It turns out that all of the predicted results are basically consistent with each other.

We introduce Grounded SAM, which uses Grounding DINO as an open-set object detector to combine with the segment anything model (SAM). This integration enables the detection and segmentation of any regions based on arbitrary text inputs and opens a door to connecting various vision models. As shown in Fig.1, a wide range of vision tasks can be achieved by using the versatile Grounded SAM pipeline. For example, an automatic annotation pipeline based solely on input images can be realized by incorporating models such as BLIP and Recognize Anything. Additionally, incorporating Stable-Diffusion allows for controllable image editing, while the integration of OSX facilitates promptable 3D human motion analysis. Grounded SAM also shows superior performance on open-vocabulary benchmarks, achieving 48.7 mean AP on SegInW (Segmentation in the wild) zero-shot benchmark with the combination of Grounding DINO-Base and SAM-Huge models.

Traditional lighting source reliability evaluations, often covering just half of a lamp's volume, can misrepresent real-world performance. To overcome these limitations,adopting advanced asset management strategies for a more holistic evaluation is crucial. This paper investigates human-centric and integrative lighting asset management in Swedish public libraries. Through field observations, interviews, and gap analysis, the study highlights a disparity between current lighting conditions and stakeholder expectations, with issues like eye strain suggesting significant improvement potential. We propose a shift towards more dynamic lighting asset management and reliability evaluations, emphasizing continuous enhancement and comprehensive training in human-centric and integrative lighting principles., Comment: 22 pages

This work targets to construct a robust human pose prior. However, it remains a persistent challenge due to biomechanical constraints and diverse human movements. Traditional priors like VAEs and NDFs often exhibit shortcomings in realism and generalization, notably with unseen noisy poses. To address these issues, we introduce DPoser, a robust and versatile human pose prior built upon diffusion models. DPoser regards various pose-centric tasks as inverse problems and employs variational diffusion sampling for efficient solving. Accordingly, designed with optimization frameworks, DPoser seamlessly benefits human mesh recovery, pose generation, pose completion, and motion denoising tasks. Furthermore, due to the disparity between the articulated poses and structured images, we propose truncated timestep scheduling to enhance the effectiveness of DPoser. Our approach demonstrates considerable enhancements over common uniform scheduling used in image domains, boasting improvements of 5.4%, 17.2%, and 3.8% across human mesh recovery, pose completion, and motion denoising, respectively. Comprehensive experiments demonstrate the superiority of DPoser over existing state-of-the-art pose priors across multiple tasks., Comment: Project Page: https://dposer.github.io; Code Released: https://github.com/moonbow721/DPoser

Humans interact with objects all the time. Enabling a humanoid to learn human-object interaction (HOI) is a key step for future smart animation and intelligent robotics systems. However, recent progress in physics-based HOI requires carefully designed task-specific rewards, making the system unscalable and labor-intensive. This work focuses on dynamic HOI imitation: teaching humanoid dynamic interaction skills through imitating kinematic HOI demonstrations. It is quite challenging because of the complexity of the interaction between body parts and objects and the lack of dynamic HOI data. To handle the above issues, we present PhysHOI, the first physics-based whole-body HOI imitation approach without task-specific reward designs. Except for the kinematic HOI representations of humans and objects, we introduce the contact graph to model the contact relations between body parts and objects explicitly. A contact graph reward is also designed, which proved to be critical for precise HOI imitation. Based on the key designs, PhysHOI can imitate diverse HOI tasks simply yet effectively without prior knowledge. To make up for the lack of dynamic HOI scenarios in this area, we introduce the BallPlay dataset that contains eight whole-body basketball skills. We validate PhysHOI on diverse HOI tasks, including whole-body grasping and basketball skills.