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Transformer-based trackers have established a dominant role in the field of visual object tracking. While these trackers exhibit promising performance, their deployment on resource-constrained devices remains challenging due to inefficiencies. To improve the inference efficiency and reduce the computation cost, prior approaches have aimed to either design lightweight trackers or distill knowledge from larger teacher models into more compact student trackers. However, these solutions often sacrifice accuracy for speed. Thus, we propose a general model compression framework for efficient transformer object tracking, named CompressTracker, to reduce the size of a pre-trained tracking model into a lightweight tracker with minimal performance degradation. Our approach features a novel stage division strategy that segments the transformer layers of the teacher model into distinct stages, enabling the student model to emulate each corresponding teacher stage more effectively. Additionally, we also design a unique replacement training technique that involves randomly substituting specific stages in the student model with those from the teacher model, as opposed to training the student model in isolation. Replacement training enhances the student model's ability to replicate the teacher model's behavior. To further forcing student model to emulate teacher model, we incorporate prediction guidance and stage-wise feature mimicking to provide additional supervision during the teacher model's compression process. Our framework CompressTracker is structurally agnostic, making it compatible with any transformer architecture. We conduct a series of experiment to verify the effectiveness and generalizability of CompressTracker. Our CompressTracker-4 with 4 transformer layers, which is compressed from OSTrack, retains about 96% performance on LaSOT (66.1% AUC) while achieves 2.17x speed up.

The integration of hardware accelerators has significantly advanced the capabilities of modern recommendation systems, enabling the exploration of complex ranking paradigms previously deemed impractical. However, the GPU-based computational costs present substantial challenges. In this paper, we demonstrate our development of an efficiency-driven approach to explore these paradigms, moving beyond traditional reliance on native PyTorch modules. We address the specific challenges posed by ranking models' dependence on categorical features, which vary in length and complicate GPU utilization. We introduce Jagged Feature Interaction Kernels, a novel method designed to extract fine-grained insights from long categorical features through efficient handling of dynamically sized tensors. We further enhance the performance of attention mechanisms by integrating Jagged tensors with Flash Attention. Our novel Jagged Flash Attention achieves up to 9x speedup and 22x memory reduction compared to dense attention. Notably, it also outperforms dense flash attention, with up to 3x speedup and 53% more memory efficiency. In production models, we observe 10% QPS improvement and 18% memory savings, enabling us to scale our recommendation systems with longer features and more complex architectures., Comment: 3 pages, 2 figures

Spin and valley are two fundamental properties of electrons in crystals. The similarity between them is well understood in valley-contrasting physics established decades ago in two-dimensional (2D) materials like graphene--with broken inversion symmetry, the two valleys in graphene exhibit opposite orbital magnetic moments, similar to the spin-1/2 behaviors of electrons, and opposite Berry curvature that leads to a half topological charge. However, valley-contrasting physics has never been explored in 3D crystals. Here, we develop a 3D acoustic crystal exhibiting 3D valley-contrasting physics. Unlike spin that is fundamentally binary, valley in 3D can take six different values, each carrying a vortex in a distinct direction. The topological valley transport is generalized from the edge states of 2D materials to the surface states of 3D materials, with interesting features including robust propagation, topological refraction, and valley-cavity localization. Our results open a new route for wave manipulation in 3D space., Comment: 8 pages, 5 figures

It was recently found that, going beyond the tendfold Altland-Zirnbauer symmetry classes and violating the bulk-boundary correspondence of the usual topological phases, PT-invariant systems support a real Chern insulator with the so-called boundary criticality, which forbids the transition between different orders of topological phases accompanied by the closing and reopening of the bulk-band gap. Here, we fnd that the periodic driving can break the boundary criticality of a PT-invariant system. Setting free from the the boundary criticality, diverse first- and second-order topological phases absent in the static case are found in both the zero and Pi/T modes. The application of our result in the three-dimensional PT-invariant system permits us to discover exotic second-order Dirac and nodal-line semimetals with coexisting surface and hinge Fermi arcs. Enriching the family of the topological phases in PT-invariant systems, our result provides us a useful way to explore novel topological phases., Comment: 7 pages and 3 figures

General expressions for one-loop contributions associated with the lepton-flavor violating decays of the standard model-like Higgs boson $h\to e_b^\pm e_a^\mp$ and gauge boson $Z\to e^\pm_b e_a^\mp$ are introduced in the unitary gauge. The results are used to discuss these decays as new physics signals in a minimal left-right symmetric model containing only one bidoublet Higgs and a $SU(2)_R$ Higgs doublet accommodating data of neutrino oscillations and $(g-2)_{\mu}$. The numerical investigation indicates that some of these decay rates can reach the near future experimental sensitivities., Comment: 39 pages, 4 figures

We introduce EXAONE 3.0 instruction-tuned language model, the first open model in the family of Large Language Models (LLMs) developed by LG AI Research. Among different model sizes, we publicly release the 7.8B instruction-tuned model to promote open research and innovations. Through extensive evaluations across a wide range of public and in-house benchmarks, EXAONE 3.0 demonstrates highly competitive real-world performance with instruction-following capability against other state-of-the-art open models of similar size. Our comparative analysis shows that EXAONE 3.0 excels particularly in Korean, while achieving compelling performance across general tasks and complex reasoning. With its strong real-world effectiveness and bilingual proficiency, we hope that EXAONE keeps contributing to advancements in Expert AI. Our EXAONE 3.0 instruction-tuned model is available at https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct

Erd\H{o}s asked whether, similarly as for graphs, the Tur\'an density of $K_4^{(3)}$ is the same as that of $K_5^{(3)-}$. This was disproved by Markstr\"om using a proof that relied on computer-aided flag algebra calculations. Here we give a flag-algebra-free proof that for all $k\geq 3$, $\pi(K_{k+1}^{(k)})<\pi(K_{k+2}^{(k)-})$. We further illustrate our methods by separating the Tur\'an densities of other hypergraphs without relying on explicit bounds. For instance, we show that $\pi(K_{\ell}^{(k)})<\pi(K_{\ell+1}^{(k)})$, for all $\ell> k\geq3$., Comment: 10 pages

The joint optimization of Neural Radiance Fields (NeRF) and camera trajectories has been widely applied in SLAM tasks due to its superior dense mapping quality and consistency. NeRF-based SLAM learns camera poses using constraints by implicit map representation. A widely observed phenomenon that results from the constraints of this form is jerky and physically unrealistic estimated camera motion, which in turn affects the map quality. To address this deficiency of current NeRF-based SLAM, we propose in this paper TS-SLAM (TS for Trajectory Smoothness). It introduces smoothness constraints on camera trajectories by representing them with uniform cubic B-splines with continuous acceleration that guarantees smooth camera motion. Benefiting from the differentiability and local control properties of B-splines, TS-SLAM can incrementally learn the control points end-to-end using a sliding window paradigm. Additionally, we regularize camera trajectories by exploiting the dynamics prior to further smooth trajectories. Experimental results demonstrate that TS-SLAM achieves superior trajectory accuracy and improves mapping quality versus NeRF-based SLAM that does not employ the above smoothness constraints.

Autonomous driving necessitates advanced object detection techniques that integrate information from multiple modalities to overcome the limitations associated with single-modal approaches. The challenges of aligning diverse data in early fusion and the complexities, along with overfitting issues introduced by deep fusion, underscore the efficacy of late fusion at the decision level. Late fusion ensures seamless integration without altering the original detector's network structure. This paper introduces a pioneering Multi-modal Multi-class Late Fusion method, designed for late fusion to enable multi-class detection. Fusion experiments conducted on the KITTI validation and official test datasets illustrate substantial performance improvements, presenting our model as a versatile solution for multi-modal object detection in autonomous driving. Moreover, our approach incorporates uncertainty analysis into the classification fusion process, rendering our model more transparent and trustworthy and providing more reliable insights into category predictions.

Length extrapolation algorithms based on Rotary position embedding (RoPE) have shown promising results in extending the context length of language models. However, understanding how position embedding can capture longer-range contextual information remains elusive. Based on the intuition that different dimensions correspond to different frequency of changes in RoPE encoding, we conducted a dimension-level analysis to investigate the correlation between a hidden dimension of an attention head and its contribution to capturing long-distance dependencies. Using our correlation metric, we identified a particular type of attention heads, which we named Positional Heads, from various length-extrapolated models. These heads exhibit a strong focus on long-range information interaction and play a pivotal role in long input processing, as evidence by our ablation. We further demonstrate the correlation between the efficiency of length extrapolation and the extension of the high-dimensional attention allocation of these heads. The identification of Positional Heads provides insights for future research in long-text comprehension.

Using $20.3\,\rm fb^{-1}$ of $e^+e^-$ collision data collected at the center-of-mass energy 3.773\,GeV with the BESIII detector, we report the first observation of the semileptonic decay $D^+\to \eta^\prime \mu^+\nu_\mu$ with significance of $8.6\sigma$ including systematic uncertainties, and an improved measurement of $D^+\to \eta^\prime e^+\nu_e$. The branching fractions of $D^+\to \eta^\prime \mu^+\nu_\mu$ and $D^+\to \eta^\prime e^+\nu_e$ are determined to be $(1.92\pm0.28_{\rm stat}\pm 0.08_{\rm syst})\times 10^{-4}$ and $(1.79\pm0.19_{\rm stat}\pm 0.07_{\rm syst})\times 10^{-4}$, respectively. From an analysis of the $D^+\to \eta^\prime \ell^+\nu_\ell$ decay dynamics, the product of the hadronic form factor $f_+^{\eta^{\prime}}(0)$ and the CKM matrix element $|V_{cd}|$ is measured for the first time, giving $f^{\eta^\prime}_+(0)|V_{cd}| = (5.92\pm0.56_{\rm stat}\pm0.13_{\rm syst})\times 10^{-2}$. No evidence for violation of $\mu-e$ lepton-flavor universality is found in both the full range and several bins of $\ell^+\nu_\ell$ four-momentum transfer. The $\eta-\eta^\prime$ mixing angle in the quark flavor basis is determined to be $\phi_{\rm P} =(39.8\pm0.8_{\rm stat}\pm0.3_{\rm syst})^\circ$.

Existing Video Corpus Moment Retrieval (VCMR) is limited to coarse-grained understanding, which hinders precise video moment localization when given fine-grained queries. In this paper, we propose a more challenging fine-grained VCMR benchmark requiring methods to localize the best-matched moment from the corpus with other partially matched candidates. To improve the dataset construction efficiency and guarantee high-quality data annotations, we propose VERIFIED, an automatic \underline{V}id\underline{E}o-text annotation pipeline to generate captions with \underline{R}el\underline{I}able \underline{FI}n\underline{E}-grained statics and \underline{D}ynamics. Specifically, we resort to large language models (LLM) and large multimodal models (LMM) with our proposed Statics and Dynamics Enhanced Captioning modules to generate diverse fine-grained captions for each video. To filter out the inaccurate annotations caused by the LLM hallucination, we propose a Fine-Granularity Aware Noise Evaluator where we fine-tune a video foundation model with disturbed hard-negatives augmented contrastive and matching losses. With VERIFIED, we construct a more challenging fine-grained VCMR benchmark containing Charades-FIG, DiDeMo-FIG, and ActivityNet-FIG which demonstrate a high level of annotation quality. We evaluate several state-of-the-art VCMR models on the proposed dataset, revealing that there is still significant scope for fine-grained video understanding in VCMR. Code and Datasets are in \href{https://github.com/hlchen23/VERIFIED}{https://github.com/hlchen23/VERIFIED}., Comment: Accepted by 38th NeurIPS Datasets & Benchmarks Track (NeurIPS 2024)

Semiconductor nanowires have shown great potential for enabling ultra-compact lasers for integrated photonics platforms. Despite the impressive progress in developing nanowire lasers, their integration into Si photonics platforms remains challenging largely due to the use of III-V and II-VI semiconductors as gain media. These materials not only have high material costs, but also require inherently complex integration with Si-based fabrication processing, increasing overall costs and thereby limiting their large-scale adoption. Furthermore, these material-based nanowire lasers rarely emit above 2 um, which is a technologically important wavelength regime for various applications in imaging and quantum sensing. Recently, group-IV nanowires, particularly direct bandgap GeSn nanowires capable of emitting above 2 um, have emerged as promising cost-effective gain media for Si-compatible nanowire lasers, but there has been no successful demonstration of lasing from this seemingly promising nanowire platform. Herein, we report the experimental observation of lasing above 2 um from a single bottom-up grown GeSn nanowire. By harnessing strain engineering and optimized cavity designs simultaneously, the single GeSn nanowire achieves an amplified material gain that can sufficiently overcome minimized optical losses, resulting in a single-mode lasing with an ultra-low threshold of ~5.3 kW cm-2. Our finding paves the way for all-group IV mid-infrared photonic-integrated circuits with compact Si-compatible lasers for on-chip classical and quantum sensing and free-space communication., Comment: 24 pages, 4 figures

The efficiency of large vision-language models (LVLMs) is constrained by the computational bottleneck of the attention mechanism during the prefill phase and the memory bottleneck of fetching the key-value (KV) cache in the decoding phase, particularly in scenarios involving high-resolution images or videos. Visual content often exhibits substantial redundancy, resulting in highly sparse attention maps within LVLMs. This sparsity can be leveraged to accelerate attention computation or compress the KV cache through various approaches. However, most studies focus on addressing only one of these bottlenecks and do not adequately support dynamic adjustment of sparsity concerning distinct layers or tasks. In this paper, we present ZipVL, an efficient inference framework designed for LVLMs that resolves both computation and memory bottlenecks through a dynamic ratio allocation strategy of important tokens. This ratio is adaptively determined based on the layer-specific distribution of attention scores, rather than fixed hyper-parameters, thereby improving efficiency for less complex tasks while maintaining high performance for more challenging ones. Then we select important tokens based on their normalized attention scores and perform attention mechanism solely on those important tokens to accelerate the prefill phase. To mitigate the memory bottleneck in the decoding phase, we employ mixed-precision quantization to the KV cache, where high-bit quantization is used for caches of important tokens, while low-bit quantization is applied to those of less importance. Our experiments demonstrate that ZipVL can accelerate the prefill phase by 2.6$\times$ and reduce GPU memory usage by 50.0%, with a minimal accuracy reduction of only 0.2% on Video-MME benchmark over LongVA-7B model, effectively enhancing the generation efficiency of LVLMs., Comment: 15 pages

Engagement estimation plays a crucial role in understanding human social behaviors, attracting increasing research interests in fields such as affective computing and human-computer interaction. In this paper, we propose a Dialogue-Aware Transformer framework (DAT) with Modality-Group Fusion (MGF), which relies solely on audio-visual input and is language-independent, for estimating human engagement in conversations. Specifically, our method employs a modality-group fusion strategy that independently fuses audio and visual features within each modality for each person before inferring the entire audio-visual content. This strategy significantly enhances the model's performance and robustness. Additionally, to better estimate the target participant's engagement levels, the introduced Dialogue-Aware Transformer considers both the participant's behavior and cues from their conversational partners. Our method was rigorously tested in the Multi-Domain Engagement Estimation Challenge held by MultiMediate'24, demonstrating notable improvements in engagement-level regression precision over the baseline model. Notably, our approach achieves a CCC score of 0.76 on the NoXi Base test set and an average CCC of 0.64 across the NoXi Base, NoXi-Add, and MPIIGI test sets., Comment: 1st Place on the NoXi Base dataset in the Multi-Domain Engagement Estimation Challenge held by MultiMediate 24, accepted by ACM Multimedia 2024. The source code is available at \url{https://github.com/MSA-LMC/DAT}

We investigate the physical properties and redshift evolution of simulated galaxies residing in protoclusters at cosmic noon, to understand the influence of the environment on galaxy formation. This work is to build clear expectations for the ongoing ODIN survey, devoted to mapping large-scale structures at z=2.4, 3.1, and 4.5 using Ly$\alpha$-emitting galaxies (LAEs) as tracers. From the IllustrisTNG simulations, we define subregions centered on the most massive clusters ranked by total stellar mass at z=0 and study the properties of galaxies within, including LAEs. To model the LAE population, we take a semi-analytical approach that assigns Ly$\alpha$ luminosity and equivalent width based on the UV luminosities to galaxies in a probabilistic manner. We investigate stellar mass, star formation rate, major mergers, and specific star formation rate of the population of star-forming galaxies and LAEs in the field and protocluster environment and trace their evolution. We find that the overall shape of the UV luminosity function (LF) in simulated protocluster environments is characterized by a shallower faint-end slope and an excess on the bright end, signaling different formation histories for galaxies therein. The difference is milder for the Ly$\alpha$ LF. While protocluster galaxies follow the same SFR-$M_{\odot}$ scaling relation as average field galaxies, a larger fraction appears to have experienced major mergers in the last 200 Myr and as a result shows enhanced star formation at a ~60% level, leading to a flatter distribution in both SFR and $M_{\odot}$ relative to galaxies in the average field. We find that protocluster galaxies, including LAEs, begin to quench much earlier (z~0.8-1.6) than field galaxies (z~0.5-0.9); our result is in agreement with recent observational results and highlights the importance of large-scale environment on the overall formation history of galaxies., Comment: 18 pages, 13 figures, to be submitted to Astronomy & Astrophysics, the abstract was reduced to meet character requirements

Current frontier video diffusion models have demonstrated remarkable results at generating high-quality videos. However, they can only generate short video clips, normally around 10 seconds or 240 frames, due to computation limitations during training. In this work, we show that existing models can be naturally extended to autoregressive video diffusion models without changing the architectures. Our key idea is to assign the latent frames with progressively increasing noise levels rather than a single noise level, which allows for fine-grained condition among the latents and large overlaps between the attention windows. Such progressive video denoising allows our models to autoregressively generate video frames without quality degradation or abrupt scene changes. We present state-of-the-art results on long video generation at 1 minute (1440 frames at 24 FPS). Videos from this paper are available at https://desaixie.github.io/pa-vdm/., Comment: 15 pages, 5 figures. Our video results and code are available at https://desaixie.github.io/pa-vdm/

This paper introduces Gaussian disorder, characterized by two parameters:the expected value and the standard deviation.Studying this type of disorder enhances our understanding of how many-body localization (MBL) transition is influenced by the central value and breadth of disorder.We apply Gaussian disorder to a one-dimensional spin-1/2 chain to reveal its features.Our findings indicate that the crucial parameter driving localization transitions is the standard deviation; as it increases, the system transitions from the ergodic phase to the localized phase, while the expected value has no effect,so the critical factor influencing MBL transition is the breadth of disorder rather than its central value.We then explore Gaussian disorder in two system-environment models (ladder and staggered configurations),analyzing the mutual influence when they are coupled.We extensively explore the impacts brought about by system disorder,environment disorder,initial state,and the strength of the sys-env interaction.Finally,for theses two different models,we consider the system and environment as a whole,studying how the localization properties of the entire system are influenced by the disorder strength of each chain and the interaction strength between the two chains.In the ladder configuration, we observe a strong dependence on the initial state,which can lead to either the loss or retention of information,whereas no such dependence on the initial state is found in the staggered configuration.We emphasize that when using dynamic indicators to analyze system characteristics, it is essential to consider the influence of different initial states,as this may lead to significant differences under certain structural conditions,and not all initial states can accurately reflect the localization properties of the many-body system.

Mosaic tiles were first introduced by Lomonaco and Kauffman in 2008 to describe quantum knots, and have since been studied for their own right. Using a modified set of tiles, front projections of Legendrian knots can be built from mosaics as well. In this work, we compute lower bounds on the mosaic number of Legendrian knots in terms of their classical invariants. We also provide a class of examples that imply sharpness of these bounds in certain cases. An additional construction of Legendrian unknots provides an upper bound on the mosaic number of Legendrian unknots. We also adapt a result of Oh, Hong, Lee, and Lee to give an algorithm to compute the number of Legendrian link mosaics of any given size. Finally, we use a computer search to provide an updated census of known mosaic numbers for Legendrian knots, including all Legendrian knots whose mosaic number is 6 or less., Comment: 46 pages, 35 figures

The schmeme of nonadiabatic holonomic quantum computation (NHQC) offers an error-resistant method for implementing quantum gates, capable of mitigating certain errors. However, the conventional NHQC schemes often entail longer operations concerning standard gate operations, making them more vulnerable to the effects of quantum decoherence. In this research, we propose an implementation of the Super-Robust NHQC scheme within the Decoherence-Free Subspace (DFS). SR-NHQC has demonstrated robustness against Global Control Errors (GCEs). By utilizing capacitance-coupled transmon qubits within a DFS, our approach enables universal gate operations on a scalable two-dimensional square lattice of superconducting qubits. Numerical simulations demonstrate the practicality of SR-NHQC in DFS, showcasing its superiority in mitigating GCEs and decoherence effects compared to conventional NHQC schemes. Our work presents a promising strategy for advancing the reliability of quantum computation in real-world applications.

We propose a quantitative direct method of proving the local stability for the trivial solution of a rough differential equation and of its regular discretization scheme. Using Doss-Sussmann technique and stopping time analysis, we prove that the trivial solution of the rough system is exponentially stable as long as the noise is small. The same conclusions hold for the regular discretization scheme with small noise and small step size. Our results are significantly stronger than \cite[Theorem 14]{garrido-atienzaetal} and \cite[Theorem 18]{GABSch18} and can be applied to non-flat bounded or linear noises.

Using $20.3~\mathrm{fb}^{-1}$ of $e^+e^-$ collision data collected at a center-of-mass energy of $E_{\rm cm}=3.773$ GeV with the BESIII detector operating at the BEPCII collider, we determine the branching fraction of the leptonic decay $D^+\to\mu^+\nu_\mu$ to be $(3.981\pm0.079_{\rm stat}\pm0.040_{\rm syst})\times10^{-4}$. Interpreting our measurement with knowledge of the Fermi coupling constant $G_F$, the masses of the $D^+$ and $\mu^+$ as well as the lifetime of the $D^+$, we determine $f_{D^+}|V_{cd}|=(47.53\pm0.48_{\rm stat}\pm0.24_{\rm syst}\pm0.12_{\rm input})~\mathrm{MeV}$. This result is a factor of 2.3 more precise than the previous best measurement. Using the value of the magnitude of the Cabibbo-Kobayashi-Maskawa matrix element $|V_{cd}|$ given by the global standard model fit, we obtain the $D^+$ decay constant $f_{D^+}=(211.5\pm2.3_{\rm stat}\pm1.1_{\rm syst}\pm0.8_{\rm input})$ MeV. Alternatively, using the value of $f_{D^+}$ from a precise lattice quantum chromodynamics calculation, we extract $|V_{cd}|=0.2242\pm0.0023_{\rm stat}\pm0.0011_{\rm syst}\pm0.0009_{\rm input}$., Comment: 9 pages, 2 figures

Motivated by recent progress in both the Josephson diode effect (JDE) and the high-temperature Josephson junction, we propose to realize the JDE in an s-wave/d-wave/s-wave (s-d-s) superconductor junction and investigate the high-temperature superconducting order parameters. The interlayer coupling between s-wave and d-wave superconductors can induce an effective $d+is$ superconducting state, spontaneously breaking time-reversal symmetry. The asymmetric s-d interlayer couplings break the inversion symmetry. Remarkably, the breaking of these two symmetries leads to a $\phi_0$-junction but does not generate JDE. We find that the emergence of the JDE in this junction depends on the $C_4$ rotational symmetry of the system. Although breaking $C_4$ rotational symmetry does not affect time-reversal and inversion symmetries, it can control the magnitude and polarity of diode efficiency. Furthermore, we propose observing C$_{4}$ symmetry breaking controlled JDE through asymmetric Shapiro steps. Our work suggests a JDE mechanism that relies on high-temperature d-wave pairing, which could inversely contribute to a potential experimental method for detecting the unconventional pairing symmetry in superconductors.

General one-loop formulas for loop-induced processes $\gamma \gamma \rightarrow \phi_i\phi_j$ with $\phi_i\phi_j = hh,~hH,~HH$ are presented in the paper. Analytic expressions evaluated in this work are valid for a class of Higgs Extensions of the Standard Models, e.g. Inert Doublet Higgs Models, Two Higgs Doublet Models, Zee-Babu Models as well as Triplet Higgs Models, etc. Analytic expressions for one-loop form factors are written in terms of the basic scalar one-loop two-, three- and four-point functions following the output format of both the packages~{\tt LoopTools} and {\tt Collier}. Physical results can be hence evaluated numerically by using one of the mentioned packages. Analytic results are tested by several checks such as the ultraviolet finiteness, infrared finiteness of the one-loop amplitudes. Furthermore, the amplitudes also obey the ward identity due to the on-shell initial photons. This identity is also verified numerically in this works. In the applications, we present the phenomenological results for Zee-Babu model as a typical example in this report. Production cross-section for the processes $\gamma \gamma\rightarrow hh$ are scanned over the parameter space of the Zee-Babu Models., Comment: 44 pages, 3 table and 11 Figures

We investigate the gravitational wave spectrum induced by first-order QCD phase transitions including the deconfinement phase transition in the pure gluon system and Friedberg-Lee model, and chiral phase transition in the quark-meson model and Polyakov quark-meson model. The gravitational wave power spectra are sensitive to the phase transition rate $\beta/H$. All QCD models predict a rather large phase transition rate in the order of $\beta/H\sim10^4$ at high temperature region, and the produced gravitational waves lie in the peak frequency region of $10^{-4}-0.01 {\rm Hz}$, corresponding to an energy spectrum in the range of $10^{-8}-10^{-7}$, which can be detected by LISA and Taiji. If a high baryon density is generated through Affleck-Dine baryogenesis or other mechanisms, the baryon chemical potential significantly reduces the phase transition rate, potentially dropping it to the order of $\beta/H\sim 10^1$, leading to the production of nanohertz gravitational waves. Furthermore, a critical quark chemical potential exists with a zero phase transition rate $\beta/H=0$, indicating that the false vacuum will not decay, thus supporting the formation of primordial quark nuggets in the early universe., Comment: 22 pages

The significant advancements in visual understanding and instruction following from Multimodal Large Language Models (MLLMs) have opened up more possibilities for broader applications in diverse and universal human-centric scenarios. However, existing image-text data may not support the precise modality alignment and integration of multi-grained information, which is crucial for human-centric visual understanding. In this paper, we introduce HERM-Bench, a benchmark for evaluating the human-centric understanding capabilities of MLLMs. Our work reveals the limitations of existing MLLMs in understanding complex human-centric scenarios. To address these challenges, we present HERM-100K, a comprehensive dataset with multi-level human-centric annotations, aimed at enhancing MLLMs' training. Furthermore, we develop HERM-7B, a MLLM that leverages enhanced training data from HERM-100K. Evaluations on HERM-Bench demonstrate that HERM-7B significantly outperforms existing MLLMs across various human-centric dimensions, reflecting the current inadequacy of data annotations used in MLLM training for human-centric visual understanding. This research emphasizes the importance of specialized datasets and benchmarks in advancing the MLLMs' capabilities for human-centric understanding.

Text-video retrieval (TVR) has seen substantial advancements in recent years, fueled by the utilization of pre-trained models and large language models (LLMs). Despite these advancements, achieving accurate matching in TVR remains challenging due to inherent disparities between video and textual modalities and irregularities in data representation. In this paper, we propose Text-Video-ProxyNet (TV-ProxyNet), a novel framework designed to decompose the conventional 1-to-N relationship of TVR into N distinct 1-to-1 relationships. By replacing a single text query with a series of text proxies, TV-ProxyNet not only broadens the query scope but also achieves a more precise expansion. Each text proxy is crafted through a refined iterative process, controlled by mechanisms we term as the director and dash, which regulate the proxy's direction and distance relative to the original text query. This setup not only facilitates more precise semantic alignment but also effectively manages the disparities and noise inherent in multimodal data. Our experiments on three representative video-text retrieval benchmarks, MSRVTT, DiDeMo, and ActivityNet Captions, demonstrate the effectiveness of TV-ProxyNet. The results show an improvement of 2.0% to 3.3% in R@1 over the baseline. TV-ProxyNet achieved state-of-the-art performance on MSRVTT and ActivityNet Captions, and a 2.0% improvement on DiDeMo compared to existing methods, validating our approach's ability to enhance semantic mapping and reduce error propensity.

Although granular materials are the second most processed in industry after water, the theoretical study of granules-structure interactions is not as advanced as that of fluid-structure interactions due to the lack of a unified view of the constitutive relation of granular materials. In the previous work, the theoretical model of granules-beam coupled vibration was developed and verified by experiments. However, it was also found that the system exhibits significant stiffness-softening Duffing characteristics even under micro-vibration, which implies that chaotic responses may be reached under certain conditions. To reveal the route and critical conditions for the system to enter chaos, in the present work, the chaotic dynamics of the system are studied. In qualitative analysis, Melnikov method is applied to analyze the instability behavior of the perturbed heteroclinic orbit of the system, thus the critical condition for the system to enter Smale horseshoe chaos, i.e., the Melnikov criterion, is obtained. The validity of the criterion is verified numerically. In experimental studies, the existence of chaotic responses and the route to chaos for granules-beam coupled vibrations is revealed. The experimental results suggest that the system response first experiences symmetry-breaking then undergoes a complete period-doubling cascade, and finally enters single-scroll chaos. In addition, although the Additional Dynamic Load (ADL) generated by granular media is highly complicated, a general and simple evolution pattern of the chaos threshold is found by parametric experiments, which is also supported by the Melnikov criterion. In short, the chaotic dynamics of granules-beam coupled vibration is revealed, which is a contribution to the engineering vibration aspect. On the theoretical side, an impressive result lies in that for the first time, the Melnikov criterion of such fractional-order..., Comment: Accepted

Fine-tuning-based unlearning methods prevail for preventing targeted harmful, sensitive, or copyrighted information within large language models while preserving overall capabilities. However, the true effectiveness of these methods is unclear. In this paper, we delve into the limitations of fine-tuning-based unlearning through activation patching and parameter restoration experiments. Our findings reveal that these methods alter the model's knowledge retrieval process, rather than genuinely erasing the problematic knowledge embedded in the model parameters. Furthermore, behavioral tests demonstrate that the unlearning mechanisms inevitably impact the global behavior of the models, affecting unrelated knowledge or capabilities. Our work advocates the development of more resilient unlearning techniques for truly erasing knowledge. Our code is released at https://github.com/yihuaihong/Dissecting-FT-Unlearning., Comment: Accepted in EMNLP 2024 Main (Short paper)

The escalating demands of compute-intensive applications, including artificial intelligence, urgently necessitate the adoption of sophisticated optical on-chip interconnect technologies to overcome critical bottlenecks in scaling future computing systems. This transition requires leveraging the inherent parallelism of wavelength and mode dimensions of light, complemented by high-order modulation formats, to significantly enhance data throughput. Here we experimentally demonstrate a novel synergy of these three dimensions, achieving multi-tens-of-terabits-per-second on-chip interconnects using ultra-broadband, multi-mode digital metamaterials. Employing a highly efficient edge-guided analog-and-digital optimization method, we inversely design foundry-compatible, robust, and multi-port digital metamaterials with an 8xhigher computational efficiency. Using a packaged five-mode multiplexing chip, we demonstrate a single-wavelength interconnect capacity of 1.62 Tbit s-1 and a record-setting multi-dimensional interconnect capacity of 38.2 Tbit s-1 across 5 modes and 88 wavelength channels. A theoretical analysis suggests that further system optimization can enable on-chip interconnects to reach sub-petabit-per-second data transmission rates. This study highlights the transformative potential of optical interconnect technologies to surmount the constraints of electronic links, thus setting the stage for next-generation datacenter and optical compute interconnects.

During remote communication, participants often share both digital and physical content, such as product designs, digital assets, and environments, to enhance mutual understanding. Recent advances in augmented communication have facilitated users to swiftly create and share digital 2D copies of physical objects from video feeds into a shared space. However, conventional 2D representations of digital objects restricts users' ability to spatially reference items in a shared immersive environment. To address this, we propose Thing2Reality, an Extended Reality (XR) communication platform that enhances spontaneous discussions of both digital and physical items during remote sessions. With Thing2Reality, users can quickly materialize ideas or physical objects in immersive environments and share them as conditioned multiview renderings or 3D Gaussians. Thing2Reality enables users to interact with remote objects or discuss concepts in a collaborative manner. Our user study revealed that the ability to interact with and manipulate 3D representations of objects significantly enhances the efficiency of discussions, with the potential to augment discussion of 2D artifacts., Comment: 18 pages (15 pages without references), 13 figures

A well-known theorem of Nikiforov asserts that any graph with a positive $K_{r}$-density contains a logarithmic blowup of $K_r$. In this paper, we explore variants of Nikiforov's result in the following form. Given $r,t\in\mathbb{N}$, when a positive $K_{r}$-density implies the existence of a significantly larger (with almost linear size) blowup of $K_t$? Our results include: For an $n$-vertex ordered graph $G$ with no induced monotone path $P_{6}$, if its complement $\overline{G}$ has positive triangle density, then $\overline{G}$ contains a biclique of size $\Omega(\frac{n}{\log{n}})$. This strengthens a recent result of Pach and Tomon. For general $k$, let $g(k)$ be the minimum $r\in \mathbb{N}$ such that for any $n$-vertex ordered graph $G$ with no induced monotone $P_{2k}$, if $\overline{G}$ has positive $K_r$-density, then $\overline{G}$ contains a biclique of size $\Omega(\frac{n}{\log{n}})$. Using concentration of measure and the isodiametric inequality on high dimensional spheres, we provide constructions showing that, surprisingly, $g(k)$ grows quadratically. On the other hand, we relate the problem of upper bounding $g(k)$ to a certain Ramsey problem and determine $g(k)$ up to a factor of 2. Any incomparability graph with positive $K_{r}$-density contains a blowup of $K_r$ of size $\Omega(\frac{n}{\log{n}}).$ This confirms a conjecture of Tomon in a stronger form. In doing so, we obtain a strong regularity type lemma for incomparability graphs with no large blowups of a clique, which is of independent interest. We also prove that any $r$-comparability graph with positive $K_{(2h-2)^{r}+1}$-density contains a blowup of $K_h$ of size $\Omega(n)$, where the constant $(2h-2)^{r}+1$ is optimal. The $\frac{n}{\log n}$ size of the blowups in all our results are optimal up to a constant factor.

A key feature of the newly discovered altermagnet is that its spin degeneracy is lifted, although it has a antiferromagnetic order and zero net magnetization. In this work, we investigate a frustrated spin-1/2 $J_1$-$J_2$-$\delta$ Heisenberg model on the square lattice by the tensor network method in combination with the linear spin-wave theory, with our focus on both the magnon excitations and longitudinal Higgs excitations. For a small $J_2$ and a finite range of $\delta$ we demonstrate that such a model hosts an altermagnetic ground state. Its magnon spectrum is split into two branches and the largest splitting occurs at $\left(\pm\pi/2, \pm\pi/2\right)$ in the Brillouin zone. The magnitudes of splitting in the two magnon modes are equal with respect to the case of $\delta=0$. Dynamical spin structure factors show that the splitting also occurs in the longitudinal Higgs modes, and the relative positions of the magnon modes and longitudinal Higgs modes in energy may change in the presence of a finite $\delta$.

Using (2712.4 $\pm$ 14.3)$\times 10^{6}$ $\psi(3686)$ events collected with the BESIII detector at BEPCII, a partial wave analysis of the decay $\psi(3686) \to \phi \eta \eta' $ is performed with the covariant tensor approach. An axial-vector state with a mass near 2.3 $\rm GeV/c^2$ is observed for the first time. Its mass and width are measured to be 2316 $\pm 9_{\mathrm{stat}} \pm 30_{\mathrm{syst}}\,\rm MeV/c^2$ and 89 $\pm 15_{\mathrm{stat}} \pm 26_{\mathrm{syst}}\,\rm MeV$, respectively. The product branching fractions of $\mathcal{B}(\psi(3686) \to X(2300) \eta') \mathcal{B}(X(2300)\to \phi \eta)$ and $\mathcal{B}(\psi(3686) \to X(2300) \eta)\mathcal{B}(X(2300)\to \phi \eta')$ are determined to be (4.8 $\pm 1.3_{\mathrm{stat}} \pm 0.7_{\mathrm{syst}})\times 10^{-6}$ and (2.2 $\pm 0.7_{\mathrm{stat}} \pm 0.7_{\mathrm{syst}})\times 10^{-6}$, respectively. The branching fraction $\mathcal{B}(\psi(3686) \to \phi \eta \eta')$ is measured for the first time to be (3.14$\pm0.17_{\mathrm{stat}}\pm0.24_{\mathrm{syst}})\times10^{-5}$. The first uncertainties are statistical and the second are systematic.

We search for the radiative decays $D^{+} \to \gamma \rho^+$ and $D^{+} \to \gamma K^{*+}$ using 20.3~fb$^{-1}$ of $e^+e^-$ annihilation data collected at the center-of-mass energy $\sqrt{s}=3.773$ GeV by the BESIII detector operating at the BEPCII collider. No significant signals are observed, and the upper limits on the branching fractions of $D^{+} \to \gamma \rho^+$ and $D^{+} \to \gamma K^{*+}$ at 90\% confidence level are set to be $1.3\times10^{-5}$ and $1.8\times10^{-5}$, respectively.

Given a graph $G$ with vertex set $V$, an outer independent Roman dominating function (OIRDF) is a function $f$ from $V(G)$ to $\{0, 1, 2\}$ for which every vertex with label $0$ under $f$ is adjacent to at least a vertex with label $2$ but not adjacent to another vertex with label $0$. The weight of an OIRDF $f$ is the sum of vertex function values all over the graph, and the minimum of an OIRDF is the outer independent Roman domination number of $G$, denoted as $\gamma_{oiR}(G)$. In this paper, we focus on the outer independent Roman domination number of the Cartesian product of paths and cycles $P_{n}\Box C_{m}$. We determine the exact values of $\gamma_{oiR}(P_n\Box C_m)$ for $n=1,2,3$ and $\gamma_{oiR}(P_n\Box C_3)$ and present an upper bound of $\gamma_{oiR}(P_n\Box C_m)$ for $n\ge 4, m\ge 4$.

A unified theoretical framework is suggested to examine the energy dissipation properties at all stages of additive implicit-explicit Runge-Kutta (IERK) methods up to fourth-order accuracy for gradient flow problems. We construct some parameterized IERK methods by applying the so-called first same as last method, that is, the diagonally implicit Runge-Kutta method with the explicit first stage and stiffly-accurate assumption for the linear stiff term, and applying the explicit Runge-Kutta method for the nonlinear term. The main part of the novel framework is to construct the differential forms and the associated differentiation matrices of IERK methods by using the difference coefficients of method and the so-called discrete orthogonal convolution kernels. As the main result, we prove that an IERK method can preserve the original energy dissipation law unconditionally if the associated differentiation matrix is positive semi-definite. The recent indicator, namely average energy dissipation rate, is also adopted for these multi-stage methods to evaluate the overall energy dissipation rate of an IERK method such that one can choose proper parameters in some parameterized IERK methods. It is found that the selection of method parameters in the IERK methods is at least as important as the selection of different IERK methods. Extensive numerical experiments are also included to support our theory., Comment: 32 pages, 3 tables, 40 figures

Large language models (LLMs), like ChatGPT, have shown that even trained with noisy prior data, they can generalize effectively to new tasks through in-context learning (ICL) and pre-training techniques. Motivated by this, we explore whether a similar approach can be applied to scientific foundation models (SFMs). Our methodology is structured as follows: (i) we collect low-cost physics-informed neural network (PINN)-based approximated prior data in the form of solutions to partial differential equations (PDEs) constructed through an arbitrary linear combination of mathematical dictionaries; (ii) we utilize Transformer architectures with self and cross-attention mechanisms to predict PDE solutions without knowledge of the governing equations in a zero-shot setting; (iii) we provide experimental evidence on the one-dimensional convection-diffusion-reaction equation, which demonstrate that pre-training remains robust even with approximated prior data, with only marginal impacts on test accuracy. Notably, this finding opens the path to pre-training SFMs with realistic, low-cost data instead of (or in conjunction with) numerical high-cost data. These results support the conjecture that SFMs can improve in a manner similar to LLMs, where fully cleaning the vast set of sentences crawled from the Internet is nearly impossible.

This article targets at unlocking the potentials of a class of prominent generative artificial intelligence (GAI) method, namely diffusion model (DM), for mobile communications. First, a DM-driven communication architecture is proposed, which introduces two key paradigms, i.e., conditional DM and DMdriven deep reinforcement learning (DRL), for wireless data generation and communication management, respectively. Then, we discuss the key advantages of DM-driven communication paradigms. To elaborate further, we explore DM-driven channel generation mechanisms for channel estimation, extrapolation, and feedback in multiple-input multiple-output (MIMO) systems. We showcase the numerical performance of conditional DM using the accurate DeepMIMO channel datasets, revealing its superiority in generating high-fidelity channels and mitigating unforeseen distribution shifts in sophisticated scenes. Furthermore, several DM-driven communication management designs are conceived, which is promising to deal with imperfect channels and taskoriented communications. To inspire future research developments, we highlight the potential applications and open research challenges of DM-driven communications. Code is available at https://github.com/xiaoxiaxusummer/GAI_COMM/, Comment: Accepted by IEEE Communications Magzine

With the growing importance of customer service in contemporary business, recognizing the intents behind service dialogues has become essential for the strategic success of enterprises. However, the nature of dialogue data varies significantly across different scenarios, and implementing an intent parser for a specific domain often involves tedious feature engineering and a heavy workload of data labeling. In this paper, we propose a novel Neural-Bayesian Program Learning model named Dialogue-Intent Parser (DI-Parser), which specializes in intent parsing under data-hungry settings and offers promising performance improvements. DI-Parser effectively utilizes data from multiple sources in a "Learning to Learn" manner and harnesses the "wisdom of the crowd" through few-shot learning capabilities on human-annotated datasets. Experimental results demonstrate that DI-Parser outperforms state-of-the-art deep learning models and offers practical advantages for industrial-scale applications.

Deep learning has emerged as a promising technology for achieving Android malware detection. To further unleash its detection potentials, software visualization can be integrated for analyzing the details of app behaviors clearly. However, facing increasingly sophisticated malware, existing visualization-based methods, analyzing from one or randomly-selected few views, can only detect limited attack types. We propose and implement LensDroid, a novel technique that detects Android malware by visualizing app behaviors from multiple complementary views. Our goal is to harness the power of combining deep learning and software visualization to automatically capture and aggregate high-level features that are not inherently linked, thereby revealing hidden maliciousness of Android app behaviors. To thoroughly comprehend the details of apps, we visualize app behaviors from three related but distinct views of behavioral sensitivities, operational contexts and supported environments. We then extract high-order semantics based on the views accordingly. To exploit semantic complementarity of the views, we design a deep neural network based model for fusing the visualized features from local to global based on their contributions to downstream tasks. A comprehensive comparison with five baseline techniques is performed on datasets of more than 51K apps in three real-world typical scenarios, including overall threats, app evolution and zero-day malware. The experimental results show that the overall performance of LensDroid is better than the baseline techniques. We also validate the complementarity of the views and demonstrate that the multi-view fusion in LensDroid enhances Android malware detection., Comment: Submitted to TIFS

The light curve of the TeV emission in GRB 221009A displays a smooth transition from an initial rapid rise to a slower rise and eventually a decay phase. The smooth temporal profile of the TeV emission suggests that it mainly results from an external shock. The temporal overlap between the prompt KeV-MeV emission and the early TeV afterglow indicates that external inverse Compton scattering (EIC) between the prompt KeV-MeV photons and the afterglow electrons is inevitable. Since the energy density of the prompt emission is much higher than that of the afterglow during the early phase, the EIC process dominates the cooling of afterglow electrons. The EIC scattering rate is influenced by the anisotropy of the seed photon field, which depends on the radii of the internal dissipation ($R_{\rm dis}$), where the prompt emission is produced, and that of the external shock ($R_{\rm ext}$), where the afterglow emission is produced. We investigate the EIC process for different values of $R_{\rm dis}/R_{\rm ext}$. We find that, for varying \( R_{\rm dis}/R_{\rm ext} \), the EIC scattering rate can differ by a factor of $\sim 2$. For GRB 221009A, the EIC emission is dominated during the early rising phase of the TeV afterglow. It then transitions to a phase dominated by the synchrotron self-Compton (SSC) emission as the intensity of the prompt emission decreases. Additionally, we investigate the effect of $\gamma \gamma$ absorption in the TeV afterglow caused by prompt MeV photons and find that it is insufficient to explain the early rapid rise in the TeV afterglow, even in the case of $R_{\rm dis}/R_{\rm ext} \sim 1$., Comment: 25 pages, 11 figures, 2 tables, comments are welcome

This paper is dedicated to clarifying and introducing the correct application of Melnikov method in fractional dynamics. Attention to the complex dynamics of hyperbolic orbits and to fractional calculus can be, respectively, traced back to Poincar\'es attack on the three-body problem a century ago and to the early days of calculus three centuries ago. Nowadays, fractional calculus has been widely applied in modeling dynamic problems across various fields due to its advantages in describing problems with non-locality. Some of these models have also been confirmed to exhibit hyperbolic orbit dynamics, and recently, they have been extensively studied based on Melnikov method, an analytical approach for homoclinic and heteroclinic orbit dynamics. Despite its decade-long application in fractional dynamics, there is a universal problem in these applications that remains to be clarified, i.e., defining fractional-order systems within finite memory boundaries leads to the neglect of perturbation calculation for parts of the stable and unstable manifolds in Melnikov analysis. After clarifying and redefining the problem, a rigorous analytical case is provided for reference. Unlike existing results, the Melnikov criterion here is derived in a globally closed form, which was previously considered unobtainable due to difficulties in the analysis of fractional-order perturbations characterized by convolution integrals with power-law type singular kernels. Finally, numerical methods are employed to verify the derived Melnikov criterion. Overall, the clarification for the problem and the presented case are expected to provide insights for future research in this topic., Comment: Accepted

The recently identified intercept $a_B$ tension of supernova (SN) magnitude-redshift relation between local ($z<0.0233$) and late-time ($z>0.0233$) Universe hints for either local-scale new physics or systematics. By comparing the intercepts of different SN groups in the PantheonPlus sample, we find the supernovae (SNe) in the third-rung distance ladder maintain a very stable $a_B$ over a wide redshift range ($0.00372\;\mathrm{km/s/Mpc}$. Therefore, the Hubble tension can be narrowed down to a tension between the Planck-CMB global constraint and the first two-rung local distance ladders, ruling out the third-rung SN systematics like a late-time transition in the SN absolute magnitude., Comment: two columns, 13 pages, 2 tables, 6 figures

In this paper, we establish a noncommutative spherical maximal inequality associated with automorphisms on von Neumann algebras, extending Magyar-Stein-Wainger's discrete spherical maximal inequality to the noncommutative setting., Comment: 16 pages

Generalized additive models (GAM) have been successfully applied to high dimensional data analysis. However, most existing methods cannot simultaneously estimate the link function, the component functions and the variable interaction. To alleviate this problem, we propose a new sparse additive model, named generalized sparse additive model with unknown link function (GSAMUL), in which the component functions are estimated by B-spline basis and the unknown link function is estimated by a multi-layer perceptron (MLP) network. Furthermore, $\ell_{2,1}$-norm regularizer is used for variable selection. The proposed GSAMUL can realize both variable selection and hidden interaction. We integrate this estimation into a bilevel optimization problem, where the data is split into training set and validation set. In theory, we provide the guarantees about the convergence of the approximate procedure. In applications, experimental evaluations on both synthetic and real world data sets consistently validate the effectiveness of the proposed approach.

Audio-visual speaker tracking aims to determine the location of human targets in a scene using signals captured by a multi-sensor platform, whose accuracy and robustness can be improved by multi-modal fusion methods. Recently, several fusion methods have been proposed to model the correlation in multiple modalities. However, for the speaker tracking problem, the cross-modal interaction between audio and visual signals hasn't been well exploited. To this end, we present a novel Speaker Tracking Network (STNet) with a deep audio-visual fusion model in this work. We design a visual-guided acoustic measurement method to fuse heterogeneous cues in a unified localization space, which employs visual observations via a camera model to construct the enhanced acoustic map. For feature fusion, a cross-modal attention module is adopted to jointly model multi-modal contexts and interactions. The correlated information between audio and visual features is further interacted in the fusion model. Moreover, the STNet-based tracker is applied to multi-speaker cases by a quality-aware module, which evaluates the reliability of multi-modal observations to achieve robust tracking in complex scenarios. Experiments on the AV16.3 and CAV3D datasets show that the proposed STNet-based tracker outperforms uni-modal methods and state-of-the-art audio-visual speaker trackers.

Van der Waals layered chalcogenide-based flexible thermoelectric devices show great potential for applications in wearable electronics. However, materials that are both highly deformable and exhibit superior thermoelectric performance are extremely limited. There is an urgent need for methods that can efficiently predict both deformability and thermoelectric performance to enable high-throughput screening of these materials. In this study, over 1000 van der Waals layered chalcogenides were high-throughput screened from material databases, the deformability of which were predicted with our previously developed deformability factor. An accurate and efficient model based on machine learning methods were developed to predict the thermoelectric properties. Several candidate materials with both deformability and thermoelectric potential were successfully discovered. Among them, NbSe2Br2 was verified by first principles calculations, achieving ZTmax value of 1.35 at 1000K, which is currently the highest value among flexible inorganic thermoelectric materials. And the power factor value of 8.1 {\mu}Wcm-1K-2 at 300K also surpassed most organic and inorganic flexible thermoelectric materials. Its high deformability mainly attributed to the small slipping energy that allows interlayer slip and the small in-plane modulus that allows deformation before failure. The high ZTmax is mainly contributed by the extremely low thermal conductivity and the high Seebeck coefficient along the out-of-plane direction at high temperature. The high power factor at room temperature is mainly comes from the high conductivity in the in-plane direction. This study is expected to accelerate the development and application of flexible thermoelectric devices based on inorganic semiconductor materials.

This paper introduces an effcient class of adaptive stencil extension reconstruction methods based on a discontinuity feedback factor, addressing the challenges of weak robustness and high computational cost in high-order schemes, particularly those of 7th-order or above. Two key innovations are presented: The accuracy order adaptively increases from the lowest level based on local stencil smoothness, contrasting with conventional methods like Weighted Essentially Non-Oscillatory (WENO) and Monotonic Upstream-Centered Scheme for Conservation Laws (MUSCL)limiters, which typically reduce order from the highest level. The Discontinuity Feedback Factor (DF) serves a dual purpose: detecting sub-cell discontinuity strength and explicitly incorporating into the reconstruction process as a local smoothness measure. This approach eliminates the need for computationally expensive smoothness indicators often required in very high-order schemes, such as 9th-order schemes, and can be easily generalized to arbitrary high-order schemes. Rigorous test cases, including a Mach 20000 jet, demonstrate the exceptional robustness of this approach., Comment: submitted to Communications in Computational Physics