Miracle-dz/UniFAR-FTUs huggingface dataset:
A Distance Metric Learning Model Based On Variational Information Bottleneck

In recent years, personalized recommendation technology has flourished and become one of the hot research directions. The matrix factorization model and the metric learning model which proposed successively have been widely studied and applied. The latter uses the Euclidean distance instead of the dot product used by the former to measure the latent space vector. While avoiding the shortcomings of the dot product, the assumption of Euclidean distance is neglected, resulting in limited recommendation quality of the model. In order to solve this problem, this paper combines the Variationl Information Bottleneck with metric learning model for the first time, and proposes a new metric learning model VIB-DML (Variational Information Bottleneck Distance Metric Learning) for rating prediction, which limits the mutual information of the latent space feature vector to improve the robustness of the model and satisfiy the assumption of Euclidean distance by decoupling the latent space feature vector. In this paper, the experimental results are compared with the root mean square error (RMSE) on the three public datasets. The results show that the generalization ability of VIB-DML is excellent. Compared with the general metric learning model MetricF, the prediction error is reduced by 7.29%. Finally, the paper proves the strong robustness of VIBDML through experiments.
Towards Geometric-Photometric Joint Alignment for Facial Mesh Registration

This paper presents a Geometric-Photometric Joint Alignment(GPJA) method, for accurately aligning human expressions by combining geometry and photometric information. Common practices for registering human heads typically involve aligning landmarks with facial template meshes using geometry processing approaches, but often overlook photometric consistency. GPJA overcomes this limitation by leveraging differentiable rendering to align vertices with target expressions, achieving joint alignment in geometry and photometric appearances automatically, without the need for semantic annotation or aligned meshes for training. It features a holistic rendering alignment strategy and a multiscale regularized optimization for robust and fast convergence. The method utilizes derivatives at vertex positions for supervision and employs a gradient-based algorithm which guarantees smoothness and avoids topological defects during the geometry evolution. Experimental results demonstrate faithful alignment under various expressions, surpassing the conventional ICP-based methods and the state-of-the-art deep learning based method. In practical, our method enhances the efficiency of obtaining topology-consistent face models from multi-view stereo facial scanning.
Non-Gaussian two mode squeezed thermal states in continuous variable quantum teleportation

While photon catalyzed two mode squeezed vacuum state has been considered in context of quantum teleportation, similar studies have not been yet conducted for photon catalyzed two-mode squeezed thermal (TMST) state. This can be attributed to challenges involved in the evaluation of teleportation fidelity for photon catalyzed TMST state. In this article, we consider a practical scheme for the implementation of non-Gaussian operation, viz., photon subtraction, photon addition, and photon catalysis, on TMST state. The generated states are employed as resources in continuous-variable quantum teleportation. The results show that the three non-Gaussian operations can enhance the teleportation fidelity. Considering the success probability of the non-Gaussian operations, we identify single-photon catalysis and single photon subtraction to be optimal for teleporting input coherent states, at low and intermediate squeezing levels.
Magnetothermopower of nodal line semimetals

The search for materials with large thermopower is of great practical interest. Dirac and Weyl semimetals have recently proven to exhibit superior thermoelectric properties, particularly when subjected to a quantizing magnetic field. Here we consider whether a similar enhancement arises in nodal line semimetals, for which the conduction and valence band meet at a line or ring in momentum space. We compute the Seebeck and Nernst coefficients for arbitrary temperature and magnetic field and we find a wealth of different scaling regimes. Most strikingly, when a sufficiently strong magnetic field is applied along the direction of the nodal line, the large degeneracy of states leads to a large, linear-in-$B$ thermopower that is temperature-independent even at low temperatures. Our results suggest that nodal line semimetals may offer significant opportunity for efficient, low-temperature thermoelectrics.
Density-based Isometric Mapping

The isometric mapping method employs the shortest path algorithm to estimate the Euclidean distance between points on High dimensional (HD) manifolds. This may not be sufficient for weakly uniformed HD data as it could lead to overestimating distances between far neighboring points, resulting in inconsistencies between the intrinsic (local) and extrinsic (global) distances during the projection. To address this issue, we modify the shortest path algorithm by adding a novel constraint inspired by the Parzen-Rosenblatt (PR) window, which helps to maintain the uniformity of the constructed shortest-path graph in Isomap. Multiple imaging datasets overall of 72,236 cases, 70,000 MINST data, 1596 from multiple Chest-XRay pneumonia datasets, and three NSCLC CT/PET datasets with a total of 640 lung cancer patients, were used to benchmark and validate PR-Isomap. 431 imaging biomarkers were extracted from each modality. Our results indicate that PR-Isomap projects HD attributes into a lower-dimensional (LD) space while preserving information, visualized by the MNIST dataset indicating the maintaining local and global distances. PR-Isomap achieved the highest comparative accuracies of 80.9% (STD:5.8) for pneumonia and 78.5% (STD:4.4), 88.4% (STD:1.4), and 61.4% (STD:11.4) for three NSCLC datasets, with a confidence interval of 95% for outcome prediction. Similarly, the multivariate Cox model showed higher overall survival, measured with c-statistics and log-likelihood test, of PR-Isomap compared to other dimensionality reduction methods. Kaplan Meier survival curve also signifies the notable ability of PR-Isomap to distinguish between high-risk and low-risk patients using multimodal imaging biomarkers preserving HD imaging characteristics for precision medicine.
Policy Space Response Oracles: A Survey

In game theory, a game refers to a model of interaction among rational decision-makers or players, making choices with the goal of achieving their individual objectives. Understanding their behavior in games is often referred to as game reasoning. This survey provides a comprehensive overview of a fast-developing game-reasoning framework for large games, known as Policy Space Response Oracles (PSRO). We first motivate PSRO, provide historical context, and position PSRO within game-reasoning approaches. We then focus on the strategy exploration issue for PSRO, the challenge of assembling an effective strategy portfolio for modeling the underlying game with minimum computational cost. We also survey current research directions for enhancing the efficiency of PSRO, and explore the applications of PSRO across various domains. We conclude by discussing open questions and future research.
Simulation-based High-Speed Elongational Rheometer for Carreau-type Materials

For the simulation-based design of fiber melt spinning processes, the accurate modeling of the processed polymer with regard to its material behavior is crucial. In this work, we develop a high-speed elongational rheometer for Carreau-type materials, making use of process simulations and fiber diameter measurements. The procedure is based on a unified formulation of the fiber spinning model for all material types (Newtonian and non-Newtonian), whose material laws are strictly monotone in the strain rate. The parametrically described material law for the elongational viscosity implies a nonlinear optimization problem for the parameter identification, for which we propose an efficient, robust gradient-based method. The work can be understood as a proof of concept, a generalization to other, more complex materials is possible.
TweetInfo: An Interactive System to Mitigate Online Harm

The increase in active users on social networking sites (SNSs) has also observed an increase in harmful content on social media sites. Harmful content is described as an inappropriate activity to harm or deceive an individual or a group of users. Alongside existing methods to detect misinformation and hate speech, users still need to be well-informed about the harmfulness of the content on SNSs. This study proposes a user-interactive system TweetInfo for mitigating the consumption of harmful content by providing metainformation about the posts. It focuses on two types of harmful content: hate speech and misinformation. TweetInfo provides insights into tweets by doing content analysis. Based on previous research, we have selected a list of metainformation. We offer the option to filter content based on metainformation Bot, Hate Speech, Misinformation, Verified Account, Sentiment, Tweet Category, Language. The proposed user interface allows customising the user's timeline to mitigate harmful content. This study present the demo version of the propose user interface of TweetInfo.
When Industry meets Trustworthy AI: A Systematic Review of AI for Industry 5.0

Industry is at the forefront of adopting new technologies, and the process followed by the adoption has a significant impact on the economy and society. In this work, we focus on analysing the current paradigm in which industry evolves, making it more sustainable and Trustworthy. In Industry 5.0, Artificial Intelligence (AI), among other technology enablers, is used to build services from a sustainable, human-centric and resilient perspective. It is crucial to understand those aspects that can bring AI to industry, respecting Trustworthy principles by collecting information to define how it is incorporated in the early stages, its impact, and the trends observed in the field. In addition, to understand the challenges and gaps in the transition from Industry 4.0 to Industry 5.0, a general perspective on the industry's readiness for new technologies is described. This provides practitioners with novel opportunities to be explored in pursuit of the adoption of Trustworthy AI in the sector.
An eternal hypersurface flow arising in centro-affine geometry

In this paper, the existence and uniqueness for a specific centro-affine invariant hypersurface flow in $R^{n+1}$ are studied, and the corresponding evolutionary processes in both centro-affine and Euclidean settings are explored. It turns out that the flow exhibits similar properties as the standard heat flow. In addition, the long time existence of the flow is investigated, which asserts that the hypersurface governed by the flow converges asymptotically toward an ellipsoid via systematically investigating evolutions of the centro-affine invariants. Furthermore, the classification of the eternal solutions for the flow is provided.
What Do Neural Networks Listen to? Exploring the Crucial Bands in Speech Enhancement Using SINC-Convolution

This study introduces a reformed Sinc-convolution (Sincconv) framework tailored for the encoder component of deep networks for speech enhancement (SE). The reformed Sincconv, based on parametrized sinc functions as band-pass filters, offers notable advantages in terms of training efficiency, filter diversity, and interpretability. The reformed Sinc-conv is evaluated in conjunction with various SE models, showcasing its ability to boost SE performance. Furthermore, the reformed Sincconv provides valuable insights into the specific frequency components that are prioritized in an SE scenario. This opens up a new direction of SE research and improving our knowledge of their operating dynamics.
$N$-jettiness soft function at next-to-next-to-leading order in perturbative QCD

We derive a compact representation of the renormalized $N$-jettiness soft function that is free of infrared and collinear divergences through next-to-next-to-leading order in perturbative QCD. The number of hard partons $N$ is a parameter in the formula for the finite remainder. Cancellation of all infrared and collinear singularities between the bare soft function and its renormalization matrix in color space is demonstrated analytically.
Low Complexity Channel Estimation for RIS-Assisted THz Systems with Beam Split

To support extremely high data rates, reconfigurable intelligent surface (RIS)-assisted terahertz (THz) communication is considered to be a promising technology for future sixth-generation networks. However, due to the typical employment of hybrid beamforming architecture in THz systems, as well as the passive nature of RIS which lacks the capability to process pilot signals, obtaining channel state information (CSI) is facing significant challenges. To accurately estimate the cascaded channel, we propose a novel low-complexity channel estimation scheme, which includes three steps. Specifically, we first estimate full CSI within a small subset of subcarriers (SCs). Then, we acquire angular information at base station and RIS based on the full CSI. Finally, we derive spatial directions and recover full-CSI for the remaining SCs. Theoretical analysis and simulation results demonstrate that the proposed scheme can achieve superior performance in terms of normalized mean-square-error and exhibit a lower computational complexity compared with the existing algorithms.
End-to-end variational quantum sensing

Harnessing quantum correlations can enable sensing beyond the classical limits of precision, with the realization of such sensors poised for transformative impacts across science and engineering. Real devices, however, face the accumulated impacts of noise effects, architecture constraints, and finite sampling rates, making the design and success of practical quantum sensors challenging. Numerical and theoretical frameworks that support the optimization and analysis of imperfections from one end of a sensing protocol through to the other (i.e., from probe state preparation through to parameter estimation) are thus crucial for translating quantum advantage into widespread practice. Here, we present an end-to-end variational framework for quantum sensing protocols, where parameterized quantum circuits and neural networks form trainable, adaptive models for quantum sensor dynamics and estimation, respectively. The framework is general and can be adapted towards arbitrary qubit architectures, as we demonstrate with experimentally-relevant ans\"atze for trapped-ion and photonic systems, and enables to directly quantify the impacts that noisy state preparation/measurement and finite data sampling have on parameter estimation. End-to-end variational frameworks can thus underpin powerful design and analysis tools for realizing quantum advantage in practical, robust sensors.
A Safe Screening Rule with Bi-level Optimization of $\nu$ Support Vector Machine

Support vector machine (SVM) has achieved many successes in machine learning, especially for a small sample problem. As a famous extension of the traditional SVM, the $\nu$ support vector machine ($\nu$-SVM) has shown outstanding performance due to its great model interpretability. However, it still faces challenges in training overhead for large-scale problems. To address this issue, we propose a safe screening rule with bi-level optimization for $\nu$-SVM (SRBO-$\nu$-SVM) which can screen out inactive samples before training and reduce the computational cost without sacrificing the prediction accuracy. Our SRBO-$\nu$-SVM is strictly deduced by integrating the Karush-Kuhn-Tucker (KKT) conditions, the variational inequalities of convex problems and the $\nu$-property. Furthermore, we develop an efficient dual coordinate descent method (DCDM) to further improve computational speed. Finally, a unified framework for SRBO is proposed to accelerate many SVM-type models, and it is successfully applied to one-class SVM. Experimental results on 6 artificial data sets and 30 benchmark data sets have verified the effectiveness and safety of our proposed methods in supervised and unsupervised tasks.
SimuCourt: Building Judicial Decision-Making Agents with Real-world Judgement Documents

With the development of deep learning, natural language processing technology has effectively improved the efficiency of various aspects of the traditional judicial industry. However, most current efforts focus solely on individual judicial stage, overlooking cross-stage collaboration. As the autonomous agents powered by large language models are becoming increasingly smart and able to make complex decisions in real-world settings, offering new insights for judicial intelligence. In this paper, (1) we introduce SimuCourt, a judicial benchmark that encompasses 420 judgment documents from real-world, spanning the three most common types of judicial cases, and a novel task Judicial Decision-Making to evaluate the judicial analysis and decision-making power of agents. To support this task, we construct a large-scale judicial knowledge base, JudicialKB, with multiple legal knowledge. (2) we propose a novel multi-agent framework, AgentsCourt. Our framework follows the real-world classic court trial process, consisting of court debate simulation, legal information retrieval and judgement refinement to simulate the decision-making of judge. (3) we perform extensive experiments, the results demonstrate that, our framework outperforms the existing advanced methods in various aspects, especially in generating legal grounds, where our model achieves significant improvements of 8.6% and 9.1% F1 score in the first and second instance settings, respectively.
Implementation of Linear Parameter Varying System to Investigate the Impact of Varying Flow Rate on the Lithium-ion Batteries Thermal Management System Performance

Battery thermal management system is an indispensable part of the electric vehicles working with Lithium-ion batteries. Accordingly, lithium-ion batteries modeling, battery heat generation, and thermal management are the main focus of researchers and car manufacturers. To fulfill the need of manufacturers in the design process, a faster model than time-consuming Computational Fluid Dynamics models (CFD) is required. Reduced Order Models (ROM) address this requirement to maintain the accuracy of CFD models while could be compiled faster. Linear Time Invariant (LTI) reduced order model has been used in the literature; however, due to the limitation of LTI system, considering the constant flow rate for the cooling fluid, a Linear Parameter Varying system with three scheduling parameters was developed in this study. It is shown that LPV system results could fit accurately to CFD results in conditions that LTI system cannot maintain accuracy. Moreover, it is shown that applying varying water flow rates could result in a smoother temperature profile.
Lightweight Object Detection: A Study Based on YOLOv7 Integrated with ShuffleNetv2 and Vision Transformer

As mobile computing technology rapidly evolves, deploying efficient object detection algorithms on mobile devices emerges as a pivotal research area in computer vision. This study zeroes in on optimizing the YOLOv7 algorithm to boost its operational efficiency and speed on mobile platforms while ensuring high accuracy. Leveraging a synergy of advanced techniques such as Group Convolution, ShuffleNetV2, and Vision Transformer, this research has effectively minimized the model's parameter count and memory usage, streamlined the network architecture, and fortified the real-time object detection proficiency on resource-constrained devices. The experimental outcomes reveal that the refined YOLO model demonstrates exceptional performance, markedly enhancing processing velocity while sustaining superior detection accuracy.
Effect of solar flares on ionospheric TEC at Varanasi, near EIA crest, during solar minimum period

A solar flare is a sudden release of energy usually near a complex group of sunspots. The interaction of solar flare radiations with constituents of ionosphere produces immediate increase in the electron density in the ionosphere. The present paper describes the effect of solar flare on ionospheric total electron contents (TEC) using global positioning system (GPS) data recorded at Varanasi, a station situated near equatorial ionization anomaly (EIA) crest region during solar minimum period. In this study, total four flares have been selected to study the effect of solar flares on ionosphere during the year 2008, which is a solar minimum period. The enhancements on ionospheric TEC have been observed during the period of solar flares. The maximum enhancement of TEC during solar flare compared to quiet mean TEC up to 24 TEC units have been observed. A significant enhancement in TEC is observed at regions around the EIA crest region during the flare in association with: (a) the flare related EUV flux enhancement and consequent increased production of ionization, and (b) changes in the equatorial electrodynamics. The magnitude of enhancement in ionospheric TEC appears to be dependent on the class of the solar flare.
DECOR: Enhancing Logic Locking Against Machine Learning-Based Attacks

Logic locking (LL) has gained attention as a promising intellectual property protection measure for integrated circuits. However, recent attacks, facilitated by machine learning (ML), have shown the potential to predict the correct key in multiple LL schemes by exploiting the correlation of the correct key value with the circuit structure. This paper presents a generic LL enhancement method based on a randomized algorithm that can significantly decrease the correlation between locked circuit netlist and correct key values in an LL scheme. Numerical results show that the proposed method can efficiently degrade the accuracy of state-of-the-art ML-based attacks down to around 50%, resulting in negligible advantage versus random guessing.
The positive energy theorem for weighted asymptotically anti-de Sitter spacetimes

The positive energy theorem for weighted asymptotically flat spin manifolds was proved by Baldauf and Ozuch \cite{BO}, and for non-spin case by Chu and Zhu \cite{CZh}. In this paper, we generalize the positive energy theorem for 3-dimensional asymptotically anti-de Sitter initial data sets to weighted asymptotically anti-de Sitter initial data sets assuming that the weighted dominant energy condition holds.
HanDiffuser: Text-to-Image Generation With Realistic Hand Appearances

Text-to-image generative models can generate high-quality humans, but realism is lost when generating hands. Common artifacts include irregular hand poses, shapes, incorrect numbers of fingers, and physically implausible finger orientations. To generate images with realistic hands, we propose a novel diffusion-based architecture called HanDiffuser that achieves realism by injecting hand embeddings in the generative process. HanDiffuser consists of two components: a Text-to-Hand-Params diffusion model to generate SMPL-Body and MANO-Hand parameters from input text prompts, and a Text-Guided Hand-Params-to-Image diffusion model to synthesize images by conditioning on the prompts and hand parameters generated by the previous component. We incorporate multiple aspects of hand representation, including 3D shapes and joint-level finger positions, orientations and articulations, for robust learning and reliable performance during inference. We conduct extensive quantitative and qualitative experiments and perform user studies to demonstrate the efficacy of our method in generating images with high-quality hands.