Miracle-dz/UniFAR-FTUs huggingface dataset:
Pareto-based Multi-Objective Recommender System with Forgetting Curve

Recommender systems with cascading architecture play an increasingly significant role in online recommendation platforms, where the approach to dealing with negative feedback is a vital issue. For instance, in short video platforms, users tend to quickly slip away from candidates that they feel aversive, and recommender systems are expected to receive these explicit negative feedbacks and make adjustments to avoid these recommendations. Considering recency effect in memories, we propose a forgetting model based on Ebbinghaus Forgetting Curve to cope with negative feedback. In addition, we introduce a Pareto optimization solver to guarantee a better trade-off between recency and model performance. In conclusion, we propose Pareto-based Multi-Objective Recommender System with forgetting curve (PMORS), which can be applied to any multi-objective recommendation and show sufficiently superiority when facing explicit negative feedback. We have conducted evaluations of PMORS and achieved favorable outcomes in short-video scenarios on both public dataset and industrial dataset. After being deployed on an online short video platform named WeChat Channels in May, 2023, PMORS has not only demonstrated promising results for both consistency and recency but also achieved an improvement of up to +1.45% GMV.
Scale-Aware Crowd Count Network with Annotation Error Correction

Traditional crowd counting networks suffer from information loss when feature maps are downsized through pooling layers, leading to inaccuracies in counting crowds at a distance. Existing methods often assume correct annotations during training, disregarding the impact of noisy annotations, especially in crowded scenes. Furthermore, the use of a fixed Gaussian kernel fails to account for the varying pixel distribution with respect to the camera distance. To overcome these challenges, we propose a Scale-Aware Crowd Counting Network (SACC-Net) that introduces a ``scale-aware'' architecture with error-correcting capabilities of noisy annotations. For the first time, we {\bf simultaneously} model labeling errors (mean) and scale variations (variance) by spatially-varying Gaussian distributions to produce fine-grained heat maps for crowd counting. Furthermore, the proposed adaptive Gaussian kernel variance enables the model to learn dynamically with a low-rank approximation, leading to improved convergence efficiency with comparable accuracy. The performance of SACC-Net is extensively evaluated on four public datasets: UCF-QNRF, UCF CC 50, NWPU, and ShanghaiTech A-B. Experimental results demonstrate that SACC-Net outperforms all state-of-the-art methods, validating its effectiveness in achieving superior crowd counting accuracy.
360 Layout Estimation via Orthogonal Planes Disentanglement and Multi-view Geometric Consistency Perception

Existing panoramic layout estimation solutions tend to recover room boundaries from a vertically compressed sequence, yielding imprecise results as the compression process often muddles the semantics between various planes. Besides, these data-driven approaches impose an urgent demand for massive data annotations, which are laborious and time-consuming. For the first problem, we propose an orthogonal plane disentanglement network (termed DOPNet) to distinguish ambiguous semantics. DOPNet consists of three modules that are integrated to deliver distortion-free, semantics-clean, and detail-sharp disentangled representations, which benefit the subsequent layout recovery. For the second problem, we present an unsupervised adaptation technique tailored for horizon-depth and ratio representations. Concretely, we introduce an optimization strategy for decision-level layout analysis and a 1D cost volume construction method for feature-level multi-view aggregation, both of which are designed to fully exploit the geometric consistency across multiple perspectives. The optimizer provides a reliable set of pseudo-labels for network training, while the 1D cost volume enriches each view with comprehensive scene information derived from other perspectives. Extensive experiments demonstrate that our solution outperforms other SoTA models on both monocular layout estimation and multi-view layout estimation tasks.
Dome structure in pressure dependence of superconducting transition temperature for HgBa$_2$Ca$_2$Cu$_3$O$_8$ -- Studies by $ab$ $initio$ low-energy effective Hamiltonian

The superconducting (SC) cuprate HgBa$_2$Ca$_2$Cu$_3$O$_8$ (Hg1223) has the highest $T_{c}^{\rm opt}\simeq 138$ K (the experimental SC transition temperature at optimal hole doping) among cuprates at ambient pressure $P_{\rm amb}$. $T_{c}^{\rm opt}$ increases under pressure $P$ and reaches $164$ K at $P_{\rm opt}\simeq 30$ GPa, then decreases with increasing $P>P_{\rm opt}$. To understand the microscopic origin of this dome-like $P$ dependence of $T_{c}^{\rm opt}$, we consider the $ab$ $initio$ low-energy effective Hamiltonian (LEH) for the antibonding (AB) Cu$3d_{x^2-y^2}$/O$2p_{\sigma}$ band. In the AB LEH for cuprates with $N_\ell \leq 2$ laminated CuO$_2$ planes between block layers, it was proposed that $T_{c}^{\rm opt}\simeq 0.16|t_1|F_{\rm SC}$, where $t_1$ is the nearest neighbor hopping and the SC order parameter $F_{\rm SC}$ at optimal hole doping mainly depends on $u=U/|t_1|$ ($U$ is the onsite effective Coulomb repulsion): $F_{\rm SC}$ is maximal at $u_{\rm opt}\simeq 8.5$ and decreases sharply with decreasing $u$ for $u<u_{\rm opt}$. In this paper, we show that (I) $|t_1|$ increases with $P$, whereas (II) $u$ decreases with $P$ in the AB LEH of Hg1223. Based on (I,II), the dome-like $P$ dependence of $T_{c}^{\rm opt}$ emerges if we assume (A) Hg1223 with $N_\ell =3$ obeys $T_{c}^{\rm opt}\simeq 0.16|t_1|F_{\rm SC}$, and (B) $u\gtrsim u_{\rm opt}$ at $P_{\rm amb}$ and $u\simeq u_{\rm opt}$ at $P_{\rm opt}$ by including expected corrections to our $ab$ $initio$ calculation. The consequence of (A,B) is the following: At $P<P_{\rm opt}$, the increase in $T_{c}^{\rm opt}$ is accounted for by (I) whereas $F_{\rm SC}$ is insensitive to (II) and hence to $P$. At $P>P_{\rm opt}$, the decrease in $T_{c}^{\rm opt}$ is accounted for by (II): The rapid decrease in $F_{\rm SC}$ with decreasing $u$ for $u<u_{\rm opt}$ dominates over (I). We support (A,B) based on studies of other cuprates.
Degrees-of-freedom penalized piecewise regression

Many popular piecewise regression models rely on minimizing a cost function on the model fit with a linear penalty on the number of segments. However, this penalty does not take into account varying complexities of the model functions on the segments potentially leading to overfitting when models with varying complexities, such as polynomials of different degrees, are used. In this work, we enhance on this approach by instead using a penalty on the sum of the degrees of freedom over all segments, called degrees-of-freedom penalized piecewise regression (DofPPR). We show that the solutions of the resulting minimization problem are unique for almost all input data in a least squares setting. We develop a fast algorithm which does not only compute a minimizer but also determines an optimal hyperparameter -- in the sense of rolling cross validation with the one standard error rule -- exactly. This eliminates manual hyperparameter selection. Our method supports optional user parameters for incorporating domain knowledge. We provide an open-source Python/Rust code for the piecewise polynomial least squares case which can be extended to further models. We demonstrate the practical utility through a simulation study and by applications to real data. A constrained variant of the proposed method gives state-of-the-art results in the Turing benchmark for unsupervised changepoint detection.
Tunable even- and odd-denominator fractional quantum Hall states in trilayer graphene

The fractional quantum Hall (FQH) states are exotic quantum many-body phases whose elementary charged excitations are neither bosons nor fermions but anyons, obeying fractional braiding statistics. While most FQH states are believed to have Abelian anyons, the Moore-Read type states with even denominators, appearing at half filling of a Landau level (LL), are predicted to possess non-Abelian excitations with appealing potentials in topological quantum computation. These states, however, depend sensitively on the orbital contents of the single-particle LL wavefunction and the mixing between different LLs. Although they have been observed in a few materials, their non-Abelian statistics still awaits experimental confirmation. Here we show magnetotransport measurements on Bernal-stacked trilayer graphene (TLG), whose unique multiband structure facilitates the interlaced LL mixing, which can be controlled by external magnetic and displacement fields. We observe a series of robust FQH states including even-denominator ones at filling factors $\nu=-9/2$, $-3/2$, $3/2$ and $9/2$. In addition, we are able to finetune the LL mixing and crossings to drive quantum phase transitions of these half-filling states and their neighboring odd-denominator ones, exhibiting a related emerging and waning behavior. Our results establish TLG as a controllable system for tuning the weights of LL orbitals and mixing strength, and a fresh platform to seek for non-Abelian quasi-particles.
GUITAR: Gradient Pruning toward Fast Neural Ranking

With the continuous popularity of deep learning and representation learning, fast vector search becomes a vital task in various ranking/retrieval based applications, say recommendation, ads ranking and question answering. Neural network based ranking is widely adopted due to its powerful capacity in modeling complex relationships, such as between users and items, questions and answers. However, it is usually exploited in offline or re-ranking manners for it is time-consuming in computations. Online neural network ranking--so called fast neural ranking--is considered challenging because neural network measures are usually non-convex and asymmetric. Traditional Approximate Nearest Neighbor (ANN) search which usually focuses on metric ranking measures, is not applicable to these advanced measures. In this paper, we introduce a novel graph searching framework to accelerate the searching in the fast neural ranking problem. The proposed graph searching algorithm is bi-level: we first construct a probable candidate set; then we only evaluate the neural network measure over the probable candidate set instead of evaluating the neural network over all neighbors. Specifically, we propose a gradient-based algorithm that approximates the rank of the neural network matching score to construct the probable candidate set; and we present an angle-based heuristic procedure to adaptively identify the proper size of the probable candidate set. Empirical results on public data confirm the effectiveness of our proposed algorithms.
PT-symmetric mapping of three states and its implementation on a cloud quantum processor

We develop a new $\mathcal{PT}$-symmetric approach for mapping three pure qubit states, implement it by the dilation method, and demonstrate it with a superconducting quantum processor provided by the IBM Quantum Experience. We derive exact formulas for the population of the post-selected $\mathcal{PT}$-symmetric subspace and show consistency with the Hermitian case, conservation of average projections on reference vectors, and Quantum Fisher Information. When used for discrimination of $N = 2$ pure states, our algorithm gives an equivalent result to the conventional unambiguous quantum state discrimination. For $N = 3$ states, our approach provides novel properties unavailable in the conventional Hermitian case and can transform an arbitrary set of three quantum states into another arbitrary set of three states at the cost of introducing an inconclusive result. For the QKD three-state protocol, our algorithm has the same error rate as the conventional minimum error, maximum confidence, and maximum mutual information strategies. The proposed method surpasses its Hermitian counterparts in quantum sensing using non-MSE metrics, providing an advantage for precise estimations within specific data space regions and improved robustness to outliers. Applied to quantum database search, our approach yields a notable decrease in circuit depth in comparison to traditional Grover's search algorithm while maintaining the same average number of oracle calls, thereby offering significant advantages for NISQ computers. Additionally, the versatility of our method can be valuable for the discrimination of highly non-symmetric quantum states, and quantum error correction. Our work unlocks new doors for applying $\mathcal{PT}$-symmetry in quantum communication, computing, and cryptography.
Discounting the Distant Future: How Much Do Uncertain Rates Increase Valuations?

Costs and benefits in the distant future—such as those associated with global warming, long-lived infrastructure, hazardous and radioactive waste, and biodiversity—often have little value today when measured with conventional discount rates. We demonstrate that when the future path of this conventional rate is uncertain and persistent (i.e., highly correlated over time), the distant future should be discounted at lower rates than suggested by the current rate. We then use two centuries of data on U.S. interest rates to quantify this effect. Using both random walk and mean-reverting models (which are indistinguishable based on historical data), we compute the certainty-equivalent rate—that is, the single discount rate that summarizes the effect of uncertainty and measures the appropriate forward rate of discount in the future. Using the random walk model, which we consider more compelling, we find that the certainty-equivalent rate falls from 3% now to 2% after 100 years, to 1% after 200 years, and down to 0.5% after 300 years. The mean-reverting model leads to a certainty-equivalent rate that remains above 3% for the next 200 years, then falls to 2% after 300 years and to 1% after 400 years. If we use these rates to value consequences at horizons of 400 years, the discounted value increases by a factor of 7,000 based on the random walk model and by a factor of 30 based on the mean-reverting model — both relative to conventional discounting. These results are relevant for a wide range of policy questions involving the distant future. Applying the random walk model to the consequences of climate change, for example, we find that inclusion of discount rate uncertainty doubles the expected present value of mitigation benefits. Other applications and alternative beliefs about the random walk–mean-reverting distinction are easily explored with our table of discount factors over time.
Vulnerability Scanners for Ethereum Smart Contracts: A Large-Scale Study

Ethereum smart contracts, which are autonomous decentralized applications on the blockchain that manage assets often exceeding millions of dollars, have become primary targets for cyberattacks. In 2023 alone, such vulnerabilities led to substantial financial losses exceeding a billion of US dollars. To counter these threats, various tools have been developed by academic and commercial entities to detect and mitigate vulnerabilities in smart contracts. Our study investigates the gap between the effectiveness of existing security scanners and the vulnerabilities that still persist in practice. We compiled four distinct datasets for this analysis. The first dataset comprises 77,219 source codes extracted directly from the blockchain, while the second includes over 4 million bytecodes obtained from Ethereum Mainnet and testnets. The other two datasets consist of nearly 14,000 manually annotated smart contracts and 373 smart contracts verified through audits, providing a foundation for a rigorous ground truth analysis on bytecode and source code. Using the unlabeled datasets, we conducted a comprehensive quantitative evaluation of 17 vulnerability scanners, revealing considerable discrepancies in their findings. Our analysis of the ground truth datasets indicated poor performance across all the tools we tested. This study unveils the reasons for poor performance and underscores that the current state of the art for smart contract security falls short in effectively addressing open problems, highlighting that the challenge of effectively detecting vulnerabilities remains a significant and unresolved issue.
Improved global well-posedness for mass-critical nonlinear Schr\"odinger equations on tori

We show new global well-posedness results for mass-critical nonlinear Schr\"odinger equations on tori in one and two dimensions. For the quintic nonlinear Schr\"odinger equation on the circle we show global well-posedness for initial data in $H^s(\mathbb{T})$ for $s>\frac{1}{3}$ and $\| u_0 \|_{L^2(\mathbb{T})} \ll 1$. In two dimensions we show global well-posedness on possibly irrational tori for $s>\frac{3}{5}$. In the focusing case we need a smallness condition for the mass, whereas in the defocusing case large mass is covered.
Adversarial Attacks on Image Classification Models: Analysis and Defense

The notion of adversarial attacks on image classification models based on convolutional neural networks (CNN) is introduced in this work. To classify images, deep learning models called CNNs are frequently used. However, when the networks are subject to adversarial attacks, extremely potent and previously trained CNN models that perform quite effectively on image datasets for image classification tasks may perform poorly. In this work, one well-known adversarial attack known as the fast gradient sign method (FGSM) is explored and its adverse effects on the performances of image classification models are examined. The FGSM attack is simulated on three pre-trained image classifier CNN architectures, ResNet-101, AlexNet, and RegNetY 400MF using randomly chosen images from the ImageNet dataset. The classification accuracies of the models are computed in the absence and presence of the attack to demonstrate the detrimental effect of the attack on the performances of the classifiers. Finally, a mechanism is proposed to defend against the FGSM attack based on a modified defensive distillation-based approach. Extensive results are presented for the validation of the proposed scheme.
LITE: A Stable Framework for Lattice-Integrated Embedding of Topological Descriptors

In this paper, we introduce a new family of descriptors for persistence diagrams. Our approach transforms these diagrams into elements of a finite-dimensional vector space using functionals based on the discrete measures they induce. While our focus is primarily on identity and frequency-based transformations, we do not restrict our approach exclusively to this types of techniques. We term this family of transformations as LITE (Lattice Integrated Topological Embedding) and prove stability for some members of this family against the 1-$Kantorovitch$-$Rubinstein$ metric, ensuring its responsiveness to subtle data variations. Extensive comparative analysis reveals that our descriptor performs competitively with the current state-of-art from the topological data analysis literature, and often surpasses, the existing methods. This research not only introduces an innovative perspective for data scientists but also critiques the current trajectory of literature on methodologies for vectorizing diagrams. It establishes a foundation for future progress in applying persistence diagrams to data analysis and machine learning under a more simple and effective lens.
Spin alignment of vector mesons by second-order hydrodynamic gradients

Starting with the polarization dependent Wigner function of vector mesons, we derive an expression for the 00-component ($\rho_{00}$) of spin density matrix in terms of the second order gradients of the vector meson distribution functions. We further apply a thermal model to analyze the transverse momentum and the azimuthal angle dependence of $\rho_{00}$ for $\phi$ and $K^{*0}$ mesons resulting from distribution gradients in Au-Au collisions with $\sqrt{s_{NN}}=130$ GeV at mid-rapidity. Our results for the transverse momentum dependence indicate that the deviations of $\rho_{00}$ from $1/3$ as the signal for spin alignment are greatly enhanced at large transverse momenta and have a strong centrality dependence while analysis of the azimuthal angle ($\phi_q$) dependence suggest that such deviations have a $\cos(2\phi_q)$ structure with opposite sign for $\phi$ and $K^{*0}$. Our finding may be considered as a baseline for probing spin-alignment mechanisms beyond hydrodynamic gradients.
Selective Inference for Sparse Graphs via Neighborhood Selection

Neighborhood selection is a widely used method used for estimating the support set of sparse precision matrices, which helps determine the conditional dependence structure in undirected graphical models. However, reporting only point estimates for the estimated graph can result in poor replicability without accompanying uncertainty estimates. In fields such as psychology, where the lack of replicability is a major concern, there is a growing need for methods that can address this issue. In this paper, we focus on the Gaussian graphical model. We introduce a selective inference method to attach uncertainty estimates to the selected (nonzero) entries of the precision matrix and decide which of the estimated edges must be included in the graph. Our method provides an exact adjustment for the selection of edges, which when multiplied with the Wishart density of the random matrix, results in valid selective inferences. Through the use of externally added randomization variables, our adjustment is easy to compute, requiring us to calculate the probability of a selection event, that is equivalent to a few sign constraints and that decouples across the nodewise regressions. Through simulations and an application to a mobile health trial designed to study mental health, we demonstrate that our selective inference method results in higher power and improved estimation accuracy.
Review of Machine Learning Approaches for Diagnostics and Prognostics of Industrial Systems Using Industrial Open Source Data

In the field of Prognostics and Health Management (PHM), recent years have witnessed a significant surge in the application of machine learning (ML). Despite this growth, the field grapples with a lack of unified guidelines and systematic approaches for effectively implementing these ML techniques and comprehensive analysis regarding industrial open-source data across varied scenarios. To address these gaps, this paper provides a comprehensive review of machine learning approaches for diagnostics and prognostics of industrial systems using open-source datasets from PHM Data Challenge Competitions held between 2018 and 2023 by PHM Society and IEEE Reliability Society and summarizes a unified ML framework. This review systematically categorizes and scrutinizes the problems, challenges, methodologies, and advancements demonstrated in these competitions, highlighting the evolving role of both conventional machine learning and deep learning in tackling complex industrial tasks related to detection, diagnosis, assessment, and prognosis. Moreover, this paper delves into the common challenges in PHM data challenge competitions by emphasizing both data-related and model-related issues and summarizes the solutions that have been employed to address these challenges. Finally, we identify key themes and potential directions for future research, providing opportunities and prospects for ML further development in PHM.