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In the paper, by convolution theorem of the Laplace transforms, a monotonicity rule for the ratio of two Laplace transforms, Bernstein's theorem for completely monotonic functions, and other analytic techniques, the authors verify decreasing property of a ratio between three derivatives of a function involving trigamma function and find necessary and sufficient conditions for a function defined by three derivatives of a function involving trigamma function to be completely monotonic. These results confirm previous guesses posed by Qi and generalize corresponding known conclusions., Comment: 12 pages

Although the performance of Temporal Action Segmentation (TAS) has improved in recent years, achieving promising results often comes with a high computational cost due to dense inputs, complex model structures, and resource-intensive post-processing requirements. To improve the efficiency while keeping the performance, we present a novel perspective centered on per-segment classification. By harnessing the capabilities of Transformers, we tokenize each video segment as an instance token, endowed with intrinsic instance segmentation. To realize efficient action segmentation, we introduce BaFormer, a boundary-aware Transformer network. It employs instance queries for instance segmentation and a global query for class-agnostic boundary prediction, yielding continuous segment proposals. During inference, BaFormer employs a simple yet effective voting strategy to classify boundary-wise segments based on instance segmentation. Remarkably, as a single-stage approach, BaFormer significantly reduces the computational costs, utilizing only 6% of the running time compared to state-of-the-art method DiffAct, while producing better or comparable accuracy over several popular benchmarks. The code for this project is publicly available at https://github.com/peiyao-w/BaFormer., Comment: 17 pages, 8 figures, 11 tables

The Internet of Things (IoT) necessitates robust access control mechanisms to secure a vast array of interconnected devices. Most of the existing IoT systems in practice use centralized solutions. We identify the problems in such solutions and adopt the blockchain based decentralized access control approach. Though there are works in the literature that use blockchain for access control, there are some gaps in these works. We develop a blockchain embedded access control (BEAC) framework to bridge the gaps. First, blockchain based solutions for access control require an enabling P2P network while existing P2P overlays do not support some required features. We develop a novel P2P infrastructure to seamlessly support our BEAC framework. Second, most of the works consider blockchain based access control for a single access control model, and we develop a generic blockchain mechanism and show that it can support the embedding of various access control models. Finally, existing works adopt existing blockchain mechanisms which may incur a high communication overhead. We develop a shortcut approach to improve the number of message rounds in the access protocol. Our experiments demonstrate the efficacy of our system, showing that the shortcut mechanism can reduces access time by approximately 43%.

Building open agents has always been the ultimate goal in AI research, and creative agents are the more enticing. Existing LLM agents excel at long-horizon tasks with well-defined goals (e.g., `mine diamonds' in Minecraft). However, they encounter difficulties on creative tasks with open goals and abstract criteria due to the inability to bridge the gap between them, thus lacking feedback for self-improvement in solving the task. In this work, we introduce autonomous embodied verification techniques for agents to fill the gap, laying the groundwork for creative tasks. Specifically, we propose the Luban agent target creative building tasks in Minecraft, which equips with two-level autonomous embodied verification inspired by human design practices: (1) visual verification of 3D structural speculates, which comes from agent synthesized CAD modeling programs; (2) pragmatic verification of the creation by generating and verifying environment-relevant functionality programs based on the abstract criteria. Extensive multi-dimensional human studies and Elo ratings show that the Luban completes diverse creative building tasks in our proposed benchmark and outperforms other baselines ($33\%$ to $100\%$) in both visualization and pragmatism. Additional demos on the real-world robotic arm show the creation potential of the Luban in the physical world.

Ocean dynamics plays a crucial role in driving global weather and climate patterns. Accurate and efficient modeling of ocean dynamics is essential for improved understanding of complex ocean circulation and processes, for predicting climate variations and their associated teleconnections, and for addressing the challenges of climate change. While great efforts have been made to improve numerical Ocean General Circulation Models (OGCMs), accurate forecasting of global oceanic variations for multi-year remains to be a long-standing challenge. Here, we introduce ORCA (Oceanic Reliable foreCAst), the first data-driven model predicting global ocean circulation from multi-year to decadal time scales. ORCA accurately simulates the three-dimensional circulations and dynamics of the global ocean with high physical consistency. Hindcasts of key oceanic variables demonstrate ORCA's remarkable prediction skills in predicting ocean variations compared with state-of-the-art numerical OGCMs and abilities in capturing occurrences of extreme events at the subsurface ocean and ENSO vertical patterns. These results demonstrate the potential of data-driven ocean models for providing cheap, efficient, and accurate global ocean modeling and prediction. Moreover, ORCA stably and faithfully emulates ocean dynamics at decadal timescales, demonstrating its potential even for climate projections. The model will be available at https://github.com/OpenEarthLab/ORCA.

Real-time rendering has been embracing ever-demanding effects, such as ray tracing. However, rendering such effects in high resolution and high frame rate remains challenging. Frame extrapolation methods, which don't introduce additional latency as opposed to frame interpolation methods such as DLSS 3 and FSR 3, boost the frame rate by generating future frames based on previous frames. However, it is a more challenging task because of the lack of information in the disocclusion regions, and recent methods also have a high engine integration cost due to requiring G-buffers as input. We propose a \emph{G-buffer free} frame extrapolation, GFFE, with a novel heuristic framework and an efficient neural network, to plausibly generate new frames in real-time without introducing additional latency. We analyze the motion of dynamic fragments and different types of disocclusions, and design the corresponding modules of the extrapolation block to handle them. After filling disocclusions, a light-weight shading correction network is used to correct shading and improve overall quality. GFFE achieves comparable or better results compared to previous interpolation as well as G-buffer-dependent extrapolation methods, with more efficient performance and easier game integration.

Quasi-twisted codes are used here as the classical ingredients in the so-called Construction X for quantum error-control codes. The construction utilizes nearly self-orthogonal codes to design quantum stabilizer codes. We expand the choices of the inner product to also cover the symplectic and trace-symplectic inner products, in addition to the original Hermitian one. A refined lower bound on the minimum distance of the resulting quantum codes is established and illustrated. We report numerous record breaking quantum codes from our randomized search for inclusion in the updated online database., Comment: 18 pages, 8 tables; see also http://codetables.de This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

The absence of adequately sufficient expert-level tumor annotations hinders the effectiveness of supervised learning based opportunistic cancer screening on medical imaging. Clinical reports (that are rich in descriptive textual details) can offer a "free lunch'' supervision information and provide tumor location as a type of weak label to cope with screening tasks, thus saving human labeling workloads, if properly leveraged. However, predicting cancer only using such weak labels can be very changeling since tumors are usually presented in small anatomical regions compared to the whole 3D medical scans. Weakly semi-supervised learning (WSSL) utilizes a limited set of voxel-level tumor annotations and incorporates alongside a substantial number of medical images that have only off-the-shelf clinical reports, which may strike a good balance between minimizing expert annotation workload and optimizing screening efficacy. In this paper, we propose a novel text-guided learning method to achieve highly accurate cancer detection results. Through integrating diagnostic and tumor location text prompts into the text encoder of a vision-language model (VLM), optimization of weakly supervised learning can be effectively performed in the latent space of VLM, thereby enhancing the stability of training. Our approach can leverage clinical knowledge by large-scale pre-trained VLM to enhance generalization ability, and produce reliable pseudo tumor masks to improve cancer detection. Our extensive quantitative experimental results on a large-scale cancer dataset, including 1,651 unique patients, validate that our approach can reduce human annotation efforts by at least 70% while maintaining comparable cancer detection accuracy to competing fully supervised methods (AUC value 0.961 versus 0.966).

Diffusion models have significantly improved the performance of image editing. Existing methods realize various approaches to achieve high-quality image editing, including but not limited to text control, dragging operation, and mask-and-inpainting. Among these, instruction-based editing stands out for its convenience and effectiveness in following human instructions across diverse scenarios. However, it still focuses on simple editing operations like adding, replacing, or deleting, and falls short of understanding aspects of world dynamics that convey the realistic dynamic nature in the physical world. Therefore, this work, EditWorld, introduces a new editing task, namely world-instructed image editing, which defines and categorizes the instructions grounded by various world scenarios. We curate a new image editing dataset with world instructions using a set of large pretrained models (e.g., GPT-3.5, Video-LLava and SDXL). To enable sufficient simulation of world dynamics for image editing, our EditWorld trains model in the curated dataset, and improves instruction-following ability with designed post-edit strategy. Extensive experiments demonstrate our method significantly outperforms existing editing methods in this new task. Our dataset and code will be available at https://github.com/YangLing0818/EditWorld, Comment: Project: https://github.com/YangLing0818/EditWorld

Advances in Large Language Models (LLMs) have led to significant interest in their potential to support human experts across a range of domains, including public health. In this work we present automated evaluations of LLMs for public health tasks involving the classification and extraction of free text. We combine six externally annotated datasets with seven new internally annotated datasets to evaluate LLMs for processing text related to: health burden, epidemiological risk factors, and public health interventions. We initially evaluate five open-weight LLMs (7-70 billion parameters) across all tasks using zero-shot in-context learning. We find that Llama-3-70B-Instruct is the highest performing model, achieving the best results on 15/17 tasks (using micro-F1 scores). We see significant variation across tasks with all open-weight LLMs scoring below 60% micro-F1 on some challenging tasks, such as Contact Classification, while all LLMs achieve greater than 80% micro-F1 on others, such as GI Illness Classification. For a subset of 12 tasks, we also evaluate GPT-4 and find comparable results to Llama-3-70B-Instruct, which scores equally or outperforms GPT-4 on 6 of the 12 tasks. Overall, based on these initial results we find promising signs that LLMs may be useful tools for public health experts to extract information from a wide variety of free text sources, and support public health surveillance, research, and interventions., Comment: 33 pages. Feedback and comments are highly appreciated

Let $S$ be a minimal irregular surface of general type, whose Albanese map induces a fibration $f:\,S \to C$ of genus $g$.We prove a linear upper bound on the genus $g$ if $K_S^2\leq 4\chi(\mathcal{O}_S)$. Examples are constructed showing that the above linear upper bound is sharp. We also give a characterization of the Albanese fibrations reaching the above upper bound when $\chi(\mathcal{O}_S)\geq 5$.On the other hand, we will construct a sequence of surfaces $S_n$ of general type with $K_{S_n}^2/\chi(\mathcal{O}_{S_n})>4$ and with an Albanese fibration $f_n$, such that the genus $g_n$ of a general fiber of $f_n$ increases quadratically with $\chi(\mathcal{O}_{S_n})$,and that $K_{S_n}^2/\chi(\mathcal{O}_{S_n})$ can be arbitrarily close to $4$., Comment: Add a characterization of the Albanese fibrations reaching the above upper bound. Comments are welcome!

Multivariate time series classification (MTSC) has attracted significant research attention due to its diverse real-world applications. Recently, exploiting transformers for MTSC has achieved state-of-the-art performance. However, existing methods focus on generic features, providing a comprehensive understanding of data, but they ignore class-specific features crucial for learning the representative characteristics of each class. This leads to poor performance in the case of imbalanced datasets or datasets with similar overall patterns but differing in minor class-specific details. In this paper, we propose a novel Shapelet Transformer (ShapeFormer), which comprises class-specific and generic transformer modules to capture both of these features. In the class-specific module, we introduce the discovery method to extract the discriminative subsequences of each class (i.e. shapelets) from the training set. We then propose a Shapelet Filter to learn the difference features between these shapelets and the input time series. We found that the difference feature for each shapelet contains important class-specific features, as it shows a significant distinction between its class and others. In the generic module, convolution filters are used to extract generic features that contain information to distinguish among all classes. For each module, we employ the transformer encoder to capture the correlation between their features. As a result, the combination of two transformer modules allows our model to exploit the power of both types of features, thereby enhancing the classification performance. Our experiments on 30 UEA MTSC datasets demonstrate that ShapeFormer has achieved the highest accuracy ranking compared to state-of-the-art methods. The code is available at https://github.com/xuanmay2701/shapeformer., Comment: Accepted at KDD 2024

We re-examine the problem of reconstructing a high-dimensional signal from a small set of linear measurements, in combination with image prior from a diffusion probabilistic model. Well-established methods for optimizing such measurements include principal component analysis (PCA), independent component analysis (ICA) and compressed sensing (CS), all of which rely on axis- or subspace-aligned statistical characterization. But many naturally occurring signals, including photographic images, contain richer statistical structure. To exploit such structure, we introduce a general method for obtaining an optimized set of linear measurements, assuming a Bayesian inverse solution that leverages the prior implicit in a neural network trained to perform denoising. We demonstrate that these measurements are distinct from those of PCA and CS, with significant improvements in minimizing squared reconstruction error. In addition, we show that optimizing the measurements for the SSIM perceptual loss leads to perceptually improved reconstruction. Our results highlight the importance of incorporating the specific statistical regularities of natural signals when designing effective linear measurements.

Measured Bidirectional Texture Function (BTF) can faithfully reproduce a realistic appearance but is costly to acquire and store due to its 6D nature (2D spatial and 4D angular). Therefore, it is practical and necessary for rendering to synthesize BTFs from a small example patch. While previous methods managed to produce plausible results, we find that they seldomly take into consideration the property of being dynamic, so a BTF must be synthesized before the rendering process, resulting in limited size, costly pre-generation and storage issues. In this paper, we propose a dynamic BTF synthesis scheme, where a BTF at any position only needs to be synthesized when being queried. Our insight is that, with the recent advances in neural dimension reduction methods, a BTF can be decomposed into disjoint low-dimensional components. We can perform dynamic synthesis only on the positional dimensions, and during rendering, recover the BTF by querying and combining these low-dimensional functions with the help of a lightweight Multilayer Perceptron (MLP). Consequently, we obtain a fully dynamic 6D BTF synthesis scheme that does not require any pre-generation, which enables efficient rendering of our infinitely large and non-repetitive BTFs on the fly. We demonstrate the effectiveness of our method through various types of BTFs taken from UBO2014.

Effective adaptation to distribution shifts in training data is pivotal for sustaining robustness in neural networks, especially when removing specific biases or outdated information, a process known as machine unlearning. Traditional approaches typically assume that data variations are random, which makes it difficult to adjust the model parameters accurately to remove patterns and characteristics from unlearned data. In this work, we present Unlearning Information Bottleneck (UIB), a novel information-theoretic framework designed to enhance the process of machine unlearning that effectively leverages the influence of systematic patterns and biases for parameter adjustment. By proposing a variational upper bound, we recalibrate the model parameters through a dynamic prior that integrates changes in data distribution with an affordable computational cost, allowing efficient and accurate removal of outdated or unwanted data patterns and biases. Our experiments across various datasets, models, and unlearning methods demonstrate that our approach effectively removes systematic patterns and biases while maintaining the performance of models post-unlearning.

Data-driven artificial intelligence (AI) models have made significant advancements in weather forecasting, particularly in medium-range and nowcasting. However, most data-driven weather forecasting models are black-box systems that focus on learning data mapping rather than fine-grained physical evolution in the time dimension. Consequently, the limitations in the temporal scale of datasets prevent these models from forecasting at finer time scales. This paper proposes a physics-AI hybrid model (i.e., WeatherGFT) which Generalizes weather forecasts to Finer-grained Temporal scales beyond training dataset. Specifically, we employ a carefully designed PDE kernel to simulate physical evolution on a small time scale (e.g., 300 seconds) and use a parallel neural networks with a learnable router for bias correction. Furthermore, we introduce a lead time-aware training framework to promote the generalization of the model at different lead times. The weight analysis of physics-AI modules indicates that physics conducts major evolution while AI performs corrections adaptively. Extensive experiments show that WeatherGFT trained on an hourly dataset, achieves state-of-the-art performance across multiple lead times and exhibits the capability to generalize 30-minute forecasts.

This paper investigates secure communications in a near-field multi-functional integrated sensing, communication, and powering (ISCAP) system with an extremely large-scale antenna arrays (ELAA) equipped at the base station (BS). In this system, the BS sends confidential messages to a single communication user (CU), and at the same time wirelessly senses a point target and charges multiple energy receivers (ERs). It is assumed that the ERs and the sensing target are potential eavesdroppers that may attempt to intercept the confidential messages intended for the CU. We consider the joint transmit beamforming design to support secure communications while ensuring the sensing and powering requirements. In particular, the BS transmits dedicated sensing/energy beams in addition to the information beam, which also play the role of artificial noise (AN) for effectively jamming potential eavesdroppers. Building upon this, we maximize the secrecy rate at the CU, subject to the maximum \ac{crb} constraints for target sensing and the minimum harvested energy constraints for the ERs. Although the formulated joint beamforming problem is non-convex and challenging to solve, we acquire the optimal solution via the semi-definite relaxation (SDR) and fractional programming techniques together with a one-dimensional (1D) search. Subsequently, we present two alternative designs based on zero-forcing (ZF) beamforming and maximum ratio transmission (MRT), respectively. Finally, our numerical results show that our proposed approaches exploit both the distance-domain resolution of near-field ELAA and the joint beamforming design for enhancing secure communication performance while ensuring the sensing and powering requirements in ISCAP, especially when the CU and the target and ER eavesdroppers are located at the same angle (but different distances) with respect to the BS., Comment: 6 pages

Finding valid light paths that involve specular vertices in Monte Carlo rendering requires solving many non-linear, transcendental equations in high-dimensional space. Existing approaches heavily rely on Newton iterations in path space, which are limited to obtaining at most a single solution each time and easily diverge when initialized with improper seeds. We propose specular polynomials, a Newton iteration-free methodology for finding a complete set of admissible specular paths connecting two arbitrary endpoints in a scene. The core is a reformulation of specular constraints into polynomial systems, which makes it possible to reduce the task to a univariate root-finding problem. We first derive bivariate systems utilizing rational coordinate mapping between the coordinates of consecutive vertices. Subsequently, we adopt the hidden variable resultant method for variable elimination, converting the problem into finding zeros of the determinant of univariate matrix polynomials. This can be effectively solved through Laplacian expansion for one bounce and a bisection solver for more bounces. Our solution is generic, completely deterministic, accurate for the case of one bounce, and GPU-friendly. We develop efficient CPU and GPU implementations and apply them to challenging glints and caustic rendering. Experiments on various scenarios demonstrate the superiority of specular polynomial-based solutions compared to Newton iteration-based counterparts., Comment: 13 pages, 13 figures, accepted by SIGGRAPH 2024

There is a pressing need for more accurate computational simulations of the opto-electronic properties of defects in materials to aid in the development of quantum sensing platforms. In this work, we explore how quantum computers could be effectively utilized for this purpose. Specifically, we develop fault-tolerant quantum algorithms to simulate optically active defect states and their radiative emission rates. We employ quantum defect embedding theory to translate the Hamiltonian of a defect-containing supercell into a smaller, effective Hamiltonian that accounts for dielectric screening effects. Our approach integrates block-encoding of the dipole operator with quantum phase estimation to selectively sample the optically active excited states that exhibit the largest dipole transition amplitudes. We also provide estimates of the quantum resources required to simulate a negatively-charged boron vacancy in a hexagonal boron nitride cluster. We conclude by offering a forward-looking perspective on the potential of quantum computers to enhance quantum sensor capabilities and identify specific scenarios where quantum computing can resolve problems traditionally challenging for classical computers., Comment: 18 pages, 4 figures, 2 tables

Consumers wish to choose sustainable accommodation for their travels, and in the case of corporations, may be required to do so. Yet accommodation marketplaces provide no meaningful capability for sustainable choice: typically CO2 estimates are provided that are identical for all accommodation of the same type across an entire country. We propose a decision support system that enables real choice of sustainable accommodation. We develop a data-driven address-specific metric called EcoGrade, which integrates government approved datasets and uses interpolation where data is sparse. We validate the metric on 10,000 UK addresses in 10 cities, showing the match of our interpolations to reality is statistically significant. We show how the metric has been embedded into a decision support system for a global accommodation marketplace and tested by real users over several months with positive user feedback. In the EU, forty percent of final energy consumption is from buildings. We need to encourage all building owners to make their accommodation more efficient. The rental sector is one area where change can occur rapidly, as rented accommodation is renovated frequently. We anticipate our decision support system using EcoGrade will encourage this positive change., Comment: 8 pages

Nowadays, humans are constantly exposed to music, whether through voluntary streaming services or incidental encounters during commercial breaks. Despite the abundance of music, certain pieces remain more memorable and often gain greater popularity. Inspired by this phenomenon, we focus on measuring and predicting music memorability. To achieve this, we collect a new music piece dataset with reliable memorability labels using a novel interactive experimental procedure. We then train baselines to predict and analyze music memorability, leveraging both interpretable features and audio mel-spectrograms as inputs. To the best of our knowledge, we are the first to explore music memorability using data-driven deep learning-based methods. Through a series of experiments and ablation studies, we demonstrate that while there is room for improvement, predicting music memorability with limited data is possible. Certain intrinsic elements, such as higher valence, arousal, and faster tempo, contribute to memorable music. As prediction techniques continue to evolve, real-life applications like music recommendation systems and music style transfer will undoubtedly benefit from this new area of research.

Using a sample of $448.1 \times 10^6$ $\psi(2S)$ events collected with the BESIII detector, we perform a study of the decay $J/\psi\rightarrow K^+K^-$ via $\psi(2S)\rightarrow \pi^+\pi^-J/\psi$. The branching fraction of $J/\psi\rightarrow K^+K^-$ is determined to be $\mathcal{B}_{K^+K^-}=(3.072\pm 0.023({\rm stat.})\pm 0.050({\rm syst.}))\times 10^{-4}$, which is consistent with previous measurements but with significantly improved precision., Comment: to be submitted to PRD

The development of Urdu scene text detection, recognition, and Visual Question Answering (VQA) technologies is crucial for advancing accessibility, information retrieval, and linguistic diversity in digital content, facilitating better understanding and interaction with Urdu-language visual data. This initiative seeks to bridge the gap between textual and visual comprehension. We propose a new multi-task Urdu scene text dataset comprising over 1000 natural scene images, which can be used for text detection, recognition, and VQA tasks. We provide fine-grained annotations for text instances, addressing the limitations of previous datasets for facing arbitrary-shaped texts. By incorporating additional annotation points, this dataset facilitates the development and assessment of methods that can handle diverse text layouts, intricate shapes, and non-standard orientations commonly encountered in real-world scenarios. Besides, the VQA annotations make it the first benchmark for the Urdu Text VQA method, which can prompt the development of Urdu scene text understanding. The proposed dataset is available at: https://github.com/Hiba-MeiRuan/Urdu-VQA-Dataset-/tree/main, Comment: Accepted by the International Conference on Document Analysis and Recognition (ICDAR) 2024

In the decades, the general field of quantum computing has experienced remarkable progress since its inception. A plethora of researchers not only proposed quantum algorithms showing the power of quantum computing but also constructed the prototype of quantum computers, making it walk into our tangible reality. Those remarkable advancements in quantum computing have opened doors for novel applications, one of which is quantum databases. Researchers are trying to use a paradigm brought by quantum computing to revolutionize various aspects of database management systems. In this paper, we envision the synergy between quantum computing and databases with two perspectives: Quantum computing-enabled technology, and quantum computing-inspired technology. Based on this classification, we present a detailed overview of the research attained in this area, aiming to show the landscape of the field and draw a road map of future directions.

In this paper, we investigate the interior H\"older regularity of solutions to the linearized Monge-Amp\`ere equation. In particular, we focus on the cases with singular right-hand side, which arise from the study of the semigeostrophic equation and singular Abreu equations. In the two-dimensional case, we give a new proof of the Caffarelli-Guti\'errez H\"older estimate (\textit{Amer. J. Math.} \textbf{119} (1997), no.\,2, 423-465) and the result of Le (\textit{Comm. Math. Phys.} \textbf{360} (2018), no.\,1, 271-305) for the linearized Monge-Amp\`ere equation with singular right-hand side term in divergence form. The main new ingredient in the proof contains the application of the partial Legendre transform to the linearized Monge-Amp\`ere equation. Building on this idea, we also establish a new Moser-Trudinger type inequality in dimension two. In higher dimensions, we derive the interior H\"older estimate under certain integrability assumptions on the coefficients using De Giorgi's iteration., Comment: 17 pages

Based on the newly acquired dense gas observations from the JCMT MALATANG survey and X-ray data from Chandra, we explore the correlation between hot gas and HCN $J=4 \rightarrow 3$, HCO$^+\ J=4 \rightarrow 3$ emission for the first time at sub-kiloparsec scale of five nearby star-forming galaxies, namely M82, M83, IC 342, NGC 253, and NGC 6946. We find that both HCN $J=4 \rightarrow 3$ and HCO$^+\ J=4 \rightarrow 3$ line luminosity show a statistically significant correlation with the 0.5${-}$2 keV X-ray emission of the diffuse hot gas ($L_{\rm 0.5 - 2\,keV}^{\rm gas}$). The Bayesian regression analysis gives the best fit of ${\rm log}(L_{\rm 0.5-2\,keV}^{\rm gas} /{\rm erg\,s^{-1}})=2.39\,{\rm log}(L'_{\rm HCN(4-3)} /{\rm K\,km\,s^{-1}\,pc^{2}})+24.83$ and ${\rm log}(L_{\rm 0.5-2\,keV}^{\rm gas} /{\rm erg\,s^{-1}})=2.48\,{\rm log}(L'_{\rm HCO^{+}(4-3)} /{\rm K\,km\,s^{-1}\,pc^{2}})+23.84$, with dispersion of $\thicksim$0.69 dex and 0.54 dex, respectively. At the sub-kiloparsec scale, we find that the power-law index of the $L_{\rm 0.5 - 2\,keV}^{\rm gas}$ ${-}$ star formation rate (SFR) relation is ${\rm log}(L_{\rm 0.5-2\,keV}^{\rm gas} /{\rm erg\,s^{-1}})=1.80\,{\rm log} ({\rm SFR} /M_\odot\,{\rm yr}^{-1})+39.16$, deviated from previous linear relations at global scale. This implies that the global property of hot gas significantly differs from individual resolved regions, which is influenced by the local physical conditions close to the sites of star formation., Comment: 10 pages, 3figures, accepted for publication in the ApJ Letters. Dedicated to Prof. Yu Gao, who initiated this work

Large language models (LLMs) have transformed the field of natural language processing, but they remain susceptible to jailbreaking attacks that exploit their capabilities to generate unintended and potentially harmful content. Existing token-level jailbreaking techniques, while effective, face scalability and efficiency challenges, especially as models undergo frequent updates and incorporate advanced defensive measures. In this paper, we introduce JailMine, an innovative token-level manipulation approach that addresses these limitations effectively. JailMine employs an automated "mining" process to elicit malicious responses from LLMs by strategically selecting affirmative outputs and iteratively reducing the likelihood of rejection. Through rigorous testing across multiple well-known LLMs and datasets, we demonstrate JailMine's effectiveness and efficiency, achieving a significant average reduction of 86% in time consumed while maintaining high success rates averaging 95%, even in the face of evolving defensive strategies. Our work contributes to the ongoing effort to assess and mitigate the vulnerability of LLMs to jailbreaking attacks, underscoring the importance of continued vigilance and proactive measures to enhance the security and reliability of these powerful language models.

In this review, the current advancements in electrochromic sensors based on two-dimensional (2D) materials with rich chemical and physical properties are critically examined. By summarizing the current trends in and prospects for utilizing multifunctional electrochromic devices (ECDs) in environmental monitoring, food quality control, medical diagnosis, and life science-related investigations, we explore the potential of using 2D materials for rational design of ECDs with compelling electrical and optical properties for biochemical sensing applications., Comment: 37 pages, 9 Figures, 2 tables

Recommender systems filter out information that meets user interests. However, users may be tired of the recommendations that are too similar to the content they have been exposed to in a short historical period, which is the so-called user fatigue. Despite the significance for a better user experience, user fatigue is seldom explored by existing recommenders. In fact, there are three main challenges to be addressed for modeling user fatigue, including what features support it, how it influences user interests, and how its explicit signals are obtained. In this paper, we propose to model user Fatigue in interest learning for sequential Recommendations (FRec). To address the first challenge, based on a multi-interest framework, we connect the target item with historical items and construct an interest-aware similarity matrix as features to support fatigue modeling. Regarding the second challenge, built upon feature cross, we propose a fatigue-enhanced multi-interest fusion to capture long-term interest. In addition, we develop a fatigue-gated recurrent unit for short-term interest learning, with temporal fatigue representations as important inputs for constructing update and reset gates. For the last challenge, we propose a novel sequence augmentation to obtain explicit fatigue signals for contrastive learning. We conduct extensive experiments on real-world datasets, including two public datasets and one large-scale industrial dataset. Experimental results show that FRec can improve AUC and GAUC up to 0.026 and 0.019 compared with state-of-the-art models, respectively. Moreover, large-scale online experiments demonstrate the effectiveness of FRec for fatigue reduction. Our codes are released at https://github.com/tsinghua-fib-lab/SIGIR24-FRec., Comment: SIGIR 2024

We create a manually-operated mechanical digger able to move within 2D densely packed granular media, in a manner intrinsically different from the existing biomimic diggers. The characteristics of our design include that the average grain size is about one tenth as large as the digger, the area packing density $\phi$ is about $90\%$ of the jamming density $\phi_{c}$, and the value of the dimensionless inertial number $I$ is in the quasi-static regime. Unlike conventional autonomous diggers that move by fluidizing the surrounding granular media with a much finer grain size, our digger swiftly bypasses this mobility mechanism as coarse grains with interparticle friction are hard to be fluidized. To achieve this, the digger has a circular shape and singly captures granular particles near the front entrance of the recess formed by the center unit running across its body using the equipped vibrating claw and then ejects the captured particles backwards. We validate this moving strategy in both experiments and numerical simulations, and demonstrate that the moving efficiency can be enhanced by the judgement of the human operator but the effectiveness is not significantly influenced by the shape of granular particles. In addition, localized vibration from the claw is needed to degrade friction between interlocked irregular-shaped particles., Comment: 9 pages, 8 figures

Recently, ray tracing has gained renewed interest with the advent of Reflective Intelligent Surfaces (RIS) technology, a key enabler of 6G wireless communications due to its capability of intelligent manipulation of electromagnetic waves. However, accurately modeling RIS-enabled wireless environments poses significant challenges due to the complex variations caused by various environmental factors and the mobility of RISs. In this paper, we propose a novel modeling approach using Neural Radiance Fields (NeRF) to characterize the dynamics of electromagnetic fields in such environments. Our method utilizes NeRF-based ray tracing to intuitively capture and visualize the complex dynamics of signal propagation, effectively modeling the complete signal pathways from the transmitter to the RIS, and from the RIS to the receiver. This two-stage process accurately characterizes multiple complex transmission paths, enhancing our understanding of signal behavior in real-world scenarios. Our approach predicts the signal field for any specified RIS placement and receiver location, facilitating efficient RIS deployment. Experimental evaluations using both simulated and real-world data validate the significant benefits of our methodology.

Animals and robots exist in a physical world and must coordinate their bodies to achieve behavioral objectives. With recent developments in deep reinforcement learning, it is now possible for scientists and engineers to obtain sensorimotor strategies (policies) for specific tasks using physically simulated bodies and environments. However, the utility of these methods goes beyond the constraints of a specific task; they offer an exciting framework for understanding the organization of an animal sensorimotor system in connection to its morphology and physical interaction with the environment, as well as for deriving general design rules for sensing and actuation in robotic systems. Algorithms and code implementing both learning agents and environments are increasingly available, but the basic assumptions and choices that go into the formulation of an embodied feedback control problem using deep reinforcement learning may not be immediately apparent. Here, we present a concise exposition of the mathematical and algorithmic aspects of model-free reinforcement learning, specifically through the use of \textit{actor-critic} methods, as a tool for investigating the feedback control underlying animal and robotic behavior., Comment: 20 pages, 3 figures

Pre-trained Large Language Models (LLM) have achieved remarkable successes in several domains. However, code-oriented LLMs are heavy in computational complexity, and quadratically with the length of the input. Toward simplifying the input program of an LLM, the state-of-the-art approach has the strategies to filter the input code tokens based on the attention scores given by the LLM. The decision to simplify the input should not rely on the attention patterns of an LLM, as these patterns are influenced by both the model architecture and the pre-training dataset. Since the model and dataset are part of the solution domain, not the problem domain where the input belongs, the outcome may differ when the model is pre-trained on a different dataset. We propose SlimCode, a model-agnostic code simplification solution for LLMs that depends on the nature of input code tokens. As an empirical study on the LLMs including CodeBERT, CodeT5, and GPT-4 for two main tasks: code search and summarization, we reported that 1) the removal ratio of code has a linear-like relation with the saving ratio on training time, 2) the impact of categorized tokens on code simplification can vary significantly, 3) the impact of categorized tokens on code simplification is task-specific but model-agnostic, and 4) the above findings hold for the paradigm-prompt engineering and interactive in-context learning. The empirical results showed that SlimCode can improve the state-of-the-art technique by 9.46% and 5.15% in terms of MRR and BLEU score on code search and summarization. Moreover, SlimCode is 133 times faster than the state-of-the-art approach. Additionally, SlimCode can reduce the cost of invoking GPT-4 by up to 24% per API query, while still producing comparable results to those with the original code.

Instruction-based image editing focuses on equipping a generative model with the capacity to adhere to human-written instructions for editing images. Current approaches typically comprehend explicit and specific instructions. However, they often exhibit a deficiency in executing active reasoning capacities required to comprehend instructions that are implicit or insufficiently defined. To enhance active reasoning capabilities and impart intelligence to the editing model, we introduce ReasonPix2Pix, a comprehensive reasoning-attentive instruction editing dataset. The dataset is characterized by 1) reasoning instruction, 2) more realistic images from fine-grained categories, and 3) increased variances between input and edited images. When fine-tuned with our dataset under supervised conditions, the model demonstrates superior performance in instructional editing tasks, independent of whether the tasks require reasoning or not. The code will be available at https://github.com/Jin-Ying/ReasonPix2Pix.

Scene text recognition is essential in many applications, including automated translation, information retrieval, driving assistance, and enhancing accessibility for individuals with visual impairments. Much research has been done to improve the accuracy and performance of scene text detection and recognition models. However, most of this research has been conducted in the most common languages, English and Chinese. There is a significant gap in low-resource languages, especially the Swahili Language. Swahili is widely spoken in East African countries but is still an under-explored language in scene text recognition. No studies have been focused explicitly on Swahili natural scene text detection and recognition, and no dataset for Swahili language scene text detection and recognition is publicly available. We propose a comprehensive dataset of Swahili scene text images and evaluate the dataset on different scene text detection and recognition models. The dataset contains 976 images collected in different places and under various circumstances. Each image has its annotation at the word level. The proposed dataset can also serve as a benchmark dataset specific to the Swahili language for evaluating and comparing different approaches and fostering future research endeavors. The dataset is available on GitHub via this link: https://github.com/FadilaW/Swahili-STR-Dataset, Comment: Accepted to ICDAR 2024

In recent years, there has been significant interest in creating 3D avatars and motions, driven by their diverse applications in areas like film-making, video games, AR/VR, and human-robot interaction. However, current efforts primarily concentrate on either generating the 3D avatar mesh alone or producing motion sequences, with integrating these two aspects proving to be a persistent challenge. Additionally, while avatar and motion generation predominantly target humans, extending these techniques to animals remains a significant challenge due to inadequate training data and methods. To bridge these gaps, our paper presents three key contributions. Firstly, we proposed a novel agent-based approach named Motion Avatar, which allows for the automatic generation of high-quality customizable human and animal avatars with motions through text queries. The method significantly advanced the progress in dynamic 3D character generation. Secondly, we introduced a LLM planner that coordinates both motion and avatar generation, which transforms a discriminative planning into a customizable Q&A fashion. Lastly, we presented an animal motion dataset named Zoo-300K, comprising approximately 300,000 text-motion pairs across 65 animal categories and its building pipeline ZooGen, which serves as a valuable resource for the community. See project website https://steve-zeyu-zhang.github.io/MotionAvatar/

Boolean satisfiability (SAT) problems are routinely solved by SAT solvers in real-life applications, yet solving time can vary drastically between solvers for the same instance. This has motivated research into machine learning models that can predict, for a given SAT instance, which solver to select among several options. Existing SAT solver selection methods all rely on some hand-picked instance features, which are costly to compute and ignore the structural information in SAT graphs. In this paper we present GraSS, a novel approach for automatic SAT solver selection based on tripartite graph representations of instances and a heterogeneous graph neural network (GNN) model. While GNNs have been previously adopted in other SAT-related tasks, they do not incorporate any domain-specific knowledge and ignore the runtime variation introduced by different clause orders. We enrich the graph representation with domain-specific decisions, such as novel node feature design, positional encodings for clauses in the graph, a GNN architecture tailored to our tripartite graphs and a runtime-sensitive loss function. Through extensive experiments, we demonstrate that this combination of raw representations and domain-specific choices leads to improvements in runtime for a pool of seven state-of-the-art solvers on both an industrial circuit design benchmark, and on instances from the 20-year Anniversary Track of the 2022 SAT Competition., Comment: Accepted by KDD 2024

Graph neural networks (GNNs) have achieved state-of-the-art performance in graph representation learning. Message passing neural networks, which learn representations through recursively aggregating information from each node and its neighbors, are among the most commonly-used GNNs. However, a wealth of structural information of individual nodes and full graphs is often ignored in such process, which restricts the expressive power of GNNs. Various graph data augmentation methods that enable the message passing with richer structure knowledge have been introduced as one main way to tackle this issue, but they are often focused on individual structure features and difficult to scale up with more structure features. In this work we propose a novel approach, namely collective structure knowledge-augmented graph neural network (CoS-GNN), in which a new message passing method is introduced to allow GNNs to harness a diverse set of node- and graph-level structure features, together with original node features/attributes, in augmented graphs. In doing so, our approach largely improves the structural knowledge modeling of GNNs in both node and graph levels, resulting in substantially improved graph representations. This is justified by extensive empirical results where CoS-GNN outperforms state-of-the-art models in various graph-level learning tasks, including graph classification, anomaly detection, and out-of-distribution generalization.

Temporal Knowledge Graph (TKG) reasoning focuses on predicting events through historical information within snapshots distributed on a timeline. Existing studies mainly concentrate on two perspectives of leveraging the history of TKGs, including capturing evolution of each recent snapshot or correlations among global historical facts. Despite the achieved significant accomplishments, these models still fall short of (1) investigating the influences of multi-granularity interactions across recent snapshots and (2) harnessing the expressive semantics of significant links accorded with queries throughout the entire history, especially events exerting a profound impact on the future. These inadequacies restrict representation ability to reflect historical dependencies and future trends thoroughly. To overcome these drawbacks, we propose an innovative TKG reasoning approach towards \textbf{His}torically \textbf{R}elevant \textbf{E}vents \textbf{S}tructuring ($\mathsf{HisRES}$). Concretely, $\mathsf{HisRES}$ comprises two distinctive modules excelling in structuring historically relevant events within TKGs, including a multi-granularity evolutionary encoder that captures structural and temporal dependencies of the most recent snapshots, and a global relevance encoder that concentrates on crucial correlations among events relevant to queries from the entire history. Furthermore, $\mathsf{HisRES}$ incorporates a self-gating mechanism for adaptively merging multi-granularity recent and historically relevant structuring representations. Extensive experiments on four event-based benchmarks demonstrate the state-of-the-art performance of $\mathsf{HisRES}$ and indicate the superiority and effectiveness of structuring historical relevance for TKG reasoning.

Strand-based hair rendering has become increasingly popular in production for its realistic appearance. However, the prevailing level-of-detail solution employing hair cards for distant hair models introduces a significant discontinuity in dynamics and appearance during the transition from strands to cards. We introduce an innovative real-time framework for strand-based hair rendering that ensures seamless transitions between different levels of detail (LOD) while maintaining a consistent hair appearance. Our method uses elliptical thick hairs that contain multiple hair strands at each LOD to maintain the shapes of hair clusters. In addition to geometric fitting, we formulate an elliptical Bidirectional Curve Scattering Distribution Functions (BCSDF) model for a thick hair, accurately capturing single scattering and multiple scattering within the hair cluster, accommodating a spectrum from sparse to dense hair distributions. Our framework, tested on various hairstyles with dynamics as well as knits, shows that it can produce highly similar appearances to full hair geometries at different viewing distances with seamless LOD transitions, while achieving up to a 3x speedup., Comment: 12 pages, 10 figures, 1 performance plot

The concept of the smoothing parameter plays a crucial role in both lattice-based and code-based cryptography, primarily due to its effectiveness in achieving nearly uniform distributions through the addition of noise. Recent research by Pathegama and Barg has determined the optimal smoothing bound for random codes under R\'enyi Divergence for any order $\alpha \in (1, \infty)$ \cite{pathegama2024r}. Considering the inherent complexity of encoding/decoding algorithms in random codes, our research introduces enhanced structural elements into these coding schemes. Specifically, this paper presents a novel derivation of the smoothing bound for random linear codes, maintaining the same order of R\'enyi Divergence and achieving optimality for any $\alpha\in (1,\infty)$. We extend this framework under KL Divergence by transitioning from random linear codes to random self-dual codes, and subsequently to random quasi-cyclic codes, incorporating progressively more structures. As an application, we derive an average-case to average-case reduction from the Learning Parity with Noise (LPN) problem to the average-case decoding problem. This reduction aligns with the parameter regime in \cite{debris2022worst}, but uniquely employs R\'enyi divergence and directly considers Bernoulli noise, instead of combining ball noise and Bernoulli noise.

In this article, we present a stabilization feedback law with integral action for conservative abstract linear systems subjected to actuator nonlinearity. Based on the designed control law, we first prove the well-posedness and global asymptotic stability of the origin of the closed-loop system by constructing a weak Lyapunov functional. Secondly, as an illustration, we apply the results to a wave equation coupled with an ordinary differential equation (ODE) at the boundary. Finally, we give the simulation results to illustrate the effectiveness of our method.

Photomultiplier tubes (PMTs) with large-area cathodes are increasingly being used in cosmic-ray experiments to enhance detection efficiency. The optical modules (OMs) of the High-Energy Underwater Neutrino Telescope (HUNT) have employed a brand new N6205 20-inch microchannel plate photomultiplier tube (MCP-PMT) developed by the North Night Vision Science & Technology (Nanjing) Research Institute Co. Ltd. (NNVT). In order to make the 20-inch PMT fit into the 23-inch diameter pressure-resistant glass sphere, NNVT improved the internal structure of PMT and shortened the height of PMT by more than 10~cm. The first batch of these PMTs has been delivered for preliminary research work. This paper describes a specific PMT testing platform built for the first batch of 15 MCP-PMTs, and some performance parameters of PMT, such as peak-to-valley ratio, TTS and nonliniearity, are measured. The measurement results show that the new PMT still has good performance and can meet the requirements of HUNT project.

We explore quantum criticality and Kibble-Zurek scaling (KZS) in the Aubry-Andre-Stark (AAS) model, where the Stark field of strength $\varepsilon$ is added onto the one-dimensional quasiperiodic lattice. We perform scaling analysis and numerical calculations of the localization length, inverse participation ratio (IPR), and energy gap between the ground and first excited states to characterize critical properties of the delocalization-localization transition. Remarkably, our scaling analysis shows that, near the critical point, the localization length $\xi$ scales with $\varepsilon$ as $\xi\propto\varepsilon^{-\nu}$ with $\nu\approx0.3$ a new critical exponent for the AAS model, which is different from the counterparts for both the pure Aubry-Andre (AA) model and Stark model. The IPR $\mathcal{I}$ scales as $\mathcal{I}\propto\varepsilon^{s}$ with the critical exponent $s\approx0.098$, which is also different from both two pure models. The energy gap $\Delta E$ scales as $\Delta E\propto \varepsilon^{\nu z}$ with the same critical exponent $z\approx2.374$ as that for the pure AA model. We further reveal hybrid scaling functions in the overlap between the critical regions of the Anderson and Stark localizations. Moreover, we investigate the driven dynamics of the localization transitions in the AAS model. By linearly changing the Stark (quasiperiodic) potential, we calculate the evolution of the localization length and the IPR, and study their dependence on the driving rate. We find that the driven dynamics from the ground state is well described by the KZS with the critical exponents obtained from the static scaling analysis. When both the Stark and quasiperiodic potentials are relevant, the KZS form includes the two scaling variables. This work extends our understanding of critical phenomena on localization transitions and generalizes the application of the KZS to hybrid models., Comment: 10 pages, 11 figures; accepted by PRB

Internal Language Model (LM)-based methods use permutation language modeling (PLM) to solve the error correction caused by conditional independence in external LM-based methods. However, random permutations of human interference cause fit oscillations in the model training, and Iterative Refinement (IR) operation to improve multimodal information decoupling also introduces additional overhead. To address these issues, this paper proposes the Hierarchical Attention autoregressive Model with Adaptive Permutation (HAAP) to enhance the location-context-image interaction capability, improving autoregressive generalization with internal LM. First, we propose Implicit Permutation Neurons (IPN) to generate adaptive attention masks to dynamically exploit token dependencies. The adaptive masks increase the diversity of training data and prevent model dependency on a specific order. It reduces the training overhead of PLM while avoiding training fit oscillations. Second, we develop Cross-modal Hierarchical Attention mechanism (CHA) to couple context and image features. This processing establishes rich positional semantic dependencies between context and image while avoiding IR. Extensive experimental results show the proposed HAAP achieves state-of-the-art (SOTA) performance in terms of accuracy, complexity, and latency on several datasets., Comment: 12 pages, 10 figures

Seismic anisotropy, arising from the crystallographic or lattice-preferred orientation of anisotropic minerals or the shape-preferred orientation of melts or cracks, can establish a critical link between Mars's past evolution and its current state. So far, although seismic anisotropy in Mars has been proposed due to different velocities of vertically and horizontally polarized shear waves in the Martian crust, obtained from crustal converted waves, multiples, and surface waves recorded by the InSight seismometer, the evidence is plausible. Notably, the shear wave splitting, which stands out as a straight indicator of seismic anisotropy, has not been reported using marsquake records. In this study, we employ Low-frequency marsquakes detected by the InSight seismometer to reveal shear wave splitting in Mars. We find that the direct S waves of three marsquake recordings (S0173a, S0235b, and S1133c) with high signal-to-noise ratios exhibit the splitting pheonmenon. We rule out the possibility of apparent anisotropy through synthetic tests, affirming the presence of seismic anisotropy in Mars. The delay time (about 1.33 s on average) measured from the direct S wave splitting is too large to be solely attributed to the seismic anisotropy in the upper crust (0 - 10 km) beneath the InSight. Thus, seismic anisotropy in the deeper region of Mars is indispensable. Combined with other geophysical evidence near the InSight landing site, the strong seismic anisotropy observed in this study implies the porous crust with aligned cracks being greater than 10 km beneath the InSight and/or the presence of an active mantle plume underneath the Elysium Planitia of Mars., Comment: Manuscript has been submitted to Earth and Planetary Science Letters; 9 figures; 33 pages

We study the problem of online unweighted bipartite matching with $n$ offline vertices and $n$ online vertices where one wishes to be competitive against the optimal offline algorithm. While the classic RANKING algorithm of Karp et al. [1990] provably attains competitive ratio of $1-1/e > 1/2$, we show that no learning-augmented method can be both 1-consistent and strictly better than $1/2$-robust under the adversarial arrival model. Meanwhile, under the random arrival model, we show how one can utilize methods from distribution testing to design an algorithm that takes in external advice about the online vertices and provably achieves competitive ratio interpolating between any ratio attainable by advice-free methods and the optimal ratio of 1, depending on the advice quality., Comment: Accepted into ICML 2024

We introduce Xmodel-VLM, a cutting-edge multimodal vision language model. It is designed for efficient deployment on consumer GPU servers. Our work directly confronts a pivotal industry issue by grappling with the prohibitive service costs that hinder the broad adoption of large-scale multimodal systems. Through rigorous training, we have developed a 1B-scale language model from the ground up, employing the LLaVA paradigm for modal alignment. The result, which we call Xmodel-VLM, is a lightweight yet powerful multimodal vision language model. Extensive testing across numerous classic multimodal benchmarks has revealed that despite its smaller size and faster execution, Xmodel-VLM delivers performance comparable to that of larger models. Our model checkpoints and code are publicly available on GitHub at https://github.com/XiaoduoAILab/XmodelVLM.

We present the first search for the leptonic decays $D^{*+}\to e^+\nu_e$ and $D^{*+}\to \mu^+\nu_\mu$ by analyzing a data sample of electron-positron collisions recorded with the BESIII detector at center-of-mass energies between 4.178 and 4.226 GeV, corresponding to an integrated luminosity of 6.32~fb$^{-1}$. No significant signal is observed. The upper limits on the branching fractions for $D^{*+}\to e^+\nu_e$ and $D^{*+}\to \mu^+\nu_\mu$ are set to be $1.1 \times 10^{-5}$ and $4.3 \times 10^{-6}$ at 90\% confidence level, respectively., Comment: 14 pages, 7 figures

Current video summarization methods rely heavily on supervised computer vision techniques, which demands time-consuming and subjective manual annotations. To overcome these limitations, we investigated self-supervised video summarization. Inspired by the success of Large Language Models (LLMs), we explored the feasibility in transforming the video summarization task into a Natural Language Processing (NLP) task. By leveraging the advantages of LLMs in context understanding, we aim to enhance the effectiveness of self-supervised video summarization. Our method begins by generating captions for individual video frames, which are then synthesized into text summaries by LLMs. Subsequently, we measure semantic distance between the captions and the text summary. Notably, we propose a novel loss function to optimize our model according to the diversity of the video. Finally, the summarized video can be generated by selecting the frames with captions similar to the text summary. Our method achieves state-of-the-art performance on the SumMe dataset in rank correlation coefficients. In addition, our method has a novel feature of being able to achieve personalized summarization.