Miracle-dz/UniFAR-FTUs huggingface dataset:
Reconfiguration of colorings in triangulations of the sphere

In 1973, Fisk proved that any $4$-coloring of a $3$-colorable triangulation of the $2$-sphere can be obtained from any $3$-coloring by a sequence of Kempe-changes. On the other hand, in the case where we are only allowed to recolor a single vertex in each step, which is a special case of a Kempe-change, there exists a $4$-coloring that cannot be obtained from any $3$-coloring. In this paper, we present a characterization of a $4$-coloring of a $3$-colorable triangulation of the $2$-sphere that can be obtained from a $3$-coloring by a sequence of recoloring operations at single vertices, and a criterion for a $3$-colorable triangulation of the $2$-sphere that all $4$-colorings can be obtained from a $3$-coloring by such a sequence. Moreover, our first result can be generalized to a high-dimensional case, in which ``$4$-coloring,'' ``$3$-colorable,'' and ``$2$-sphere'' above are replaced with ``$k$-coloring,'' ``$(k-1)$-colorable,'' and ``$(k-2)$-sphere'' for $k \geq 4$, respectively. In addition, we show that the problem of deciding whether, for given two $(k+1)$-colorings, one can be obtained from the other by such a sequence is PSPACE-complete for any fixed $k \geq 4$. Our results above can be rephrased as new results on the computational problems named {\sc $k$-Recoloring} and {\sc Connectedness of $k$-Coloring Reconfiguration Graph}, which are fundamental problems in the field of combinatorial reconfiguration.
A Critical Reflection and Forward Perspective on Empathy and Natural Language Processing

We review the state of research on empathy in natural language processing and identify the following issues: (1) empathy definitions are absent or abstract, which (2) leads to low construct validity and reproducibility. Moreover, (3) emotional empathy is overemphasized, skewing our focus to a narrow subset of simplified tasks. We believe these issues hinder research progress and argue that current directions will benefit from a clear conceptualization that includes operationalizing cognitive empathy components. Our main objectives are to provide insight and guidance on empathy conceptualization for NLP research objectives and to encourage researchers to pursue the overlooked opportunities in this area, highly relevant, e.g., for clinical and educational sectors.
SoK: Modeling Explainability in Security Monitoring for Trust, Privacy, and Interpretability

—Trust, privacy, and interpretability have emerged as signiﬁcant concerns for experts deploying deep learning models for security monitoring. Due to their back-box nature, these models cannot provide an intuitive understanding of the machine learning predictions, which are crucial in several decision-making applications, like anomaly detection. Security operations centers have a number of security monitoring tools that analyze logs and generate threat alerts which security analysts inspect. The alerts lack sufﬁcient explanation on why it was raised or the context in which they occurred. Existing explanation methods for security also suffer from low ﬁdelity and low stability and ignore privacy concerns. However, explanations are highly desirable; therefore, we systematize this knowledge on explanation models so they can ensure trust and privacy in security monitoring. Through our collaborative study of security operation centers, security monitoring tools, and explanation techniques, we discuss the strengths of existing methods and concerns vis-a-vis applications, such as security log analysis. We present a pipeline to design interpretable and privacy-preserving system monitoring tools. Additionally, we deﬁne and propose quantitative metrics to evaluate methods in explainable security. Finally, we discuss challenges and enlist exciting research directions for explorations.
Multi-view Multi-label Anomaly Network Traffic Classification based on MLP-Mixer Neural Network

Network traffic classification is the basis of many network security applications and has attracted enough attention in the field of cyberspace security. Existing network traffic classification based on convolutional neural networks (CNNs) often emphasizes local patterns of traffic data while ignoring global information associations. In this paper, we propose an MLP-Mixer based multi-view multi-label neural network for network traffic classification. Compared with the existing CNN-based methods, our method adopts the MLP-Mixer structure, which is more in line with the structure of the packet than the conventional convolution operation. In our method, one packet is divided into the packet header and the packet body, together with the flow features of the packet as input from different views. We utilize a multi-label setting to learn different scenarios simultaneously to improve the classification performance by exploiting the correlations between different scenarios. Taking advantage of the above characteristics, we propose an end-to-end network traffic classification method. We conduct experiments on three public datasets, and the experimental results show that our method can achieve superior performance.
Efficient Similarity-Based Passive Filter Pruning for Compressing CNNS

Convolution neural networks (CNNs) have shown great success in various applications. However, the computational complexity and memory storage of CNNs is a bottleneck for their deployment on resource-constrained devices. Recent efforts towards reducing the computation cost and the memory overhead of CNNs involve similarity-based passive filter pruning methods. Similarity-based passive filter pruning methods compute a pairwise similarity matrix for the filters and eliminate a few similar filters to obtain a small pruned CNN. However, the computational complexity of computing the pairwise similarity matrix is high, particularly when a convolutional layer has many filters. To reduce the computational complexity in obtaining the pairwise similarity matrix, we propose to use an efficient method where the complete pairwise similarity matrix is approximated from only a few of its columns by using a Nyström approximation method. The proposed efficient similarity-based passive filter pruning method is 3 times faster and gives same accuracy at the same reduction in computations for CNNs compared to that of the similarity-based pruning method that computes a complete pairwise similarity matrix. Apart from this, the proposed efficient similarity-based pruning method performs similarly or better than the existing norm-based pruning methods. The efficacy of the proposed pruning method is evaluated on CNNs such as DCASE 2021 Task 1A baseline network and a VGGish network designed for acoustic scene classification.
Masses and decay constants of $B_c^{(*)}$ mesons with $N_f =2+1+1$ twisted mass fermions

We present a preliminary lattice determination of the masses and decay constants of the pseudoscalar and vector mesons $B_c$ and $B_c^*$. Our analysis is based on the gauge configurations produced by the European Twisted Mass Collaboration with $N_f = 2 + 1 + 1$ flavors of dynamical quarks. We simulated at three different values of the lattice spacing and with pion masses as small as 210 MeV. Heavy-quark masses are simulated directly on the lattice up to $\sim 3$ times the physical charm mass. The physical b-quark mass is reached using the ETMC ratio method. Our preliminary results are: $M_{B_c} = 6341\,(60)$ MeV, $f_{B_c} = 396\,(12)$ MeV, $M_{B_c^*} / M_{B_c} = 1.0037\,(39)$ and $f_{B_c^*} / f_{B_c} = 0.987\,(7)$.
Validity Assessment of Legal Will Statements as Natural Language Inference

This work introduces a natural language inference (NLI) dataset that focuses on the validity of statements in legal wills. This dataset is unique because: (a) each entailment decision requires three inputs: the statement from the will, the law, and the conditions that hold at the time of the testator's death; and (b) the included texts are longer than the ones in current NLI datasets. We trained eight neural NLI models in this dataset. All the models achieve more than 80% macro F1 and accuracy, which indicates that neural approaches can handle this task reasonably well. However, group accuracy, a stricter evaluation measure that is calculated with a group of positive and negative examples generated from the same statement as a unit, is in mid 80s at best, which suggests that the models' understanding of the task remains superficial. Further ablative analyses and explanation experiments indicate that all three text segments are used for prediction, but some decisions rely on semantically irrelevant tokens. This indicates that overfitting on these longer texts likely happens, and that additional research is required for this task to be solved.
Theoretical expectations for bulk flows in large-scale surveys

We calculate the theoretical expectation for the bulk motion of a large-scale survey of the type recently carried out by Lauer and Postman. Included are the effects of survey geometry, errors in the distance measurements, clustering properties of the sample, and different assumed power spectra. We considered the power spectrum calculated from the Infrared Astronomy Satellite (IRAS)-QDOT survey, as well as spectra from hot + cold and standard cold dark matter models. We find that measurement uncertainty, sparse sampling, and clustering can lead to a much larger expectation for the bulk motion of a cluster sample than for the volume as a whole. However, our results suggest that the expected bulk motion is still inconsistent with that reported by Lauer and Postman at the 95%-97% confidence level.
DyG2Vec: Efficient Representation Learning for Dynamic Graphs

Temporal graph neural networks have shown promising results in learning inductive representations by automatically extracting temporal patterns. However, previous works often rely on complex memory modules or inefficient random walk methods to construct temporal representations. To address these limitations, we present an efficient yet effective attention-based encoder that leverages temporal edge encodings and window-based subgraph sampling to generate task-agnostic embeddings. Moreover, we propose a joint-embedding architecture using non-contrastive SSL to learn rich temporal embeddings without labels. Experimental results on 7 benchmark datasets indicate that on average, our model outperforms SoTA baselines on the future link prediction task by 4.23% for the transductive setting and 3.30% for the inductive setting while only requiring 5-10x less training/inference time. Lastly, different aspects of the proposed framework are investigated through experimental analysis and ablation studies. The code is publicly available at https://github.com/huawei-noah/noah-research/tree/master/graph_atlas.
Counterfactual Data Augmentation via Perspective Transition for Open-Domain Dialogues

The construction of open-domain dialogue systems requires high-quality dialogue datasets. The dialogue data admits a wide variety of responses for a given dialogue history, especially responses with different semantics. However, collecting high-quality such a dataset in most scenarios is labor-intensive and time-consuming. In this paper, we propose a data augmentation method to automatically augment high-quality responses with different semantics by counterfactual inference. Specifically, given an observed dialogue, our counterfactual generation model first infers semantically different responses by replacing the observed reply perspective with substituted ones. Furthermore, our data selection method filters out detrimental augmented responses. Experimental results show that our data augmentation method can augment high-quality responses with different semantics for a given dialogue history, and can outperform competitive baselines on multiple downstream tasks.
Non-Robustness of the Cluster-Robust Inference: with a Proposal of a New Robust Method

The conventional cluster-robust (CR) standard errors may not be robust. They are vulnerable to data that contain a small number of large clusters. When a researcher uses the 51 states in the U.S. as clusters, the largest cluster (California) consists of about 10% of the total sample. Such a case in fact violates the assumptions under which the widely used CR methods are guaranteed to work. We formally show that the conventional CR methods fail if the distribution of cluster sizes follows a power law with exponent less than two. Besides the example of 51 state clusters, some examples are drawn from a list of recent original research articles published in a top journal. In light of these negative results about the existing CR methods, we propose a weighted CR (WCR) method as a simple fix. Simulation studies support our arguments that the WCR method is robust while the conventional CR methods are not.
Depletion and low gas temperature in the L183 (= L134N) prestellar core: the N2H + - N2D + tool ⋆

Context. The study of pre-stellar cores (PSCs) suffers from a lack of undepleted species to trace the gas physical properties in their very dense inner parts. Aims. We want to carry out detailed modelling of N2H+ and N2D+ cuts across the L183 main core to evaluate the depletion of these species and their usefulness as a probe of physical conditions in PSCs. Methods. We have developed a non-LTE (NLTE) Monte-Carlo code treating the 1D radiative transfer of both N2H+ and N2D+ , making use of recently published collisional coefficients with He between individual hyperfine levels. The code includes line overlap between hyperfine transitions. An extensive set of core models is calculated and compared with observations. Special attention is paid to the issue of source coupling to the antenna beam. Results. The best fitting models indicate that i) gas in the core center is very cold (7± 1 K) and thermalized with dust, ii) depletion of N2H+ does occur, starting at densities 5–7E5 cm? 3 and reaching a factor of 6 (+13/? 3) in abundance, iii) deuterium fractionation reaches ? 70% at the core center, and iv) the density profile is proportional to r^-1 out to ? 4000 AU, and to r^? 2 beyond. Conclusions. Our NLTE code could be used to (re-)interpret recent and upcoming observations of N2H+ and N2D+ in many pre-stellar cores of interest, to obtain better temperature and abundance profiles.
ImplantFormer: Vision Transformer based Implant Position Regression Using Dental CBCT Data

Implant prosthesis is the most appropriate treatment for dentition defect or dentition loss, which usually involves a surgical guide design process to decide the implant position. However, such design heavily relies on the subjective experiences of dentists. In this paper, a transformer-based Implant Position Regression Network, ImplantFormer, is proposed to automatically predict the implant position based on the oral CBCT data. We creatively propose to predict the implant position using the 2D axial view of the tooth crown area and fit a centerline of the implant to obtain the actual implant position at the tooth root. Convolutional stem and decoder are designed to coarsely extract image features before the operation of patch embedding and integrate multi-level feature maps for robust prediction, respectively. As both long-range relationship and local features are involved, our approach can better represent global information and achieves better location performance. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed ImplantFormer achieves superior performance than existing methods.
The Florence 4D Facial Expression Dataset

Human facial expressions change dynamically, so their recognition / analysis should be conducted by accounting for the temporal evolution of face deformations either in 2D or 3D. While abundant 2D video data do exist, this is not the case in 3D, where few 3D dynamic (4D) datasets were released for public use. The negative consequence of this scarcity of data is amplified by current deep learning based-methods for facial expression analysis that require large quantities of variegate samples to be effectively trained. With the aim of smoothing such limitations, in this paper we propose a large dataset, named Florence 4D, composed of dynamic sequences of 3D face models, where a combination of synthetic and real identities exhibit an unprecedented variety of 4D facial expressions, with variations that include the classical neutral-apex transition, but generalize to expression-to-expression. All these characteristics are not exposed by any of the existing 4D datasets and they cannot even be obtained by combining more than one dataset. We strongly believe that making such a data corpora publicly available to the community will allow designing and experimenting new applications that were not possible to investigate till now. To show at some extent the difficulty of our data in terms of different identities and varying expressions, we also report a baseline experimentation on the proposed dataset that can be used as baseline.
Lipschitz regularized gradient flows and latent generative particles

Lipschitz regularized f -divergences are constructed by imposing a bound on the Lipschitz constant of the discriminator in the variational representation. These divergences interpolate between the Wasserstein metric and f -divergences and provide a flexible family of loss functions for non-absolutely continuous (e.g. empirical) distributions, possibly with heavy tails. We first construct Lipschitz regularized gradient flows on the space of probability measures based on these divergences. Examples of such gradient flows are Lipschitz regularized Fokker-Planck and porous medium partial differential equations (PDEs) for the Kullback-Leibler and α-divergences, respectively. The regularization corresponds to imposing a Courant–Friedrichs–Lewy numerical stability condition on the PDEs. For empirical measures, the Lipschitz regularization on gradient flows induces a numerically stable transporter/discriminator particle algorithm, where the generative particles are transported along the gradient of the discriminator. The gradient structure leads to a regularized Fisher information which is the total kinetic energy of the particles and can be used to track the convergence of the algorithm. The Lipschitz regularized discriminator can be implemented via neural network spectral normalization and the particle algorithm generates approximate samples from possibly high-dimensional distributions known only from data. Notably, our particle algorithm can generate synthetic data even in small sample size regimes. A new data processing inequality for the regularized divergence allows us to combine our particle algorithm with representation learning, e.g. autoencoder architectures. The resulting particle algorithm in latent space yields markedly improved generative properties in terms of efficiency and quality of the synthetic samples. From a statistical mechanics perspective the encoding can be interpreted dynamically as learning a better mobility for the generative particles.
Shared Manifold Learning Using a Triplet Network for Multiple Sensor Translation and Fusion With Missing Data

Heterogeneous data fusion can enhance the robustness and accuracy of an algorithm on a given task. However, due to the difference in various modalities, aligning the sensors and embedding their information into discriminative and compact representations is challenging. In this article, we propose a contrastive learning-based multimodal alignment network to align data from different sensors into a shared and discriminative manifold where class information is preserved. The proposed architecture uses a multimodal triplet autoencoder to cluster the latent space in such a way that samples of the same classes from each heterogeneous modality are mapped close to each other. Since all the modalities exist in a shared manifold, a unified classification framework is proposed. The resulting latent space representations are fused to perform more robust and accurate classification. In a missing sensor scenario, the latent space of one sensor is easily and efficiently predicted using another sensor's latent space, thereby allowing sensor translation. We conducted extensive experiments on a manually labeled multimodal dataset containing hyperspectral data from AVIRIS-NG and NEON and light detection and ranging (LiDAR) data from NEON. Finally, the model is validated on two benchmark datasets: Berlin Dataset (hyperspectral and synthetic aperture radar) and MUUFL Gulfport Dataset (hyperspectral and LiDAR). A comparison made with other methods demonstrates the superiority of this method. We achieved a mean overall accuracy of 94.3% on the MUUFL dataset and the best overall accuracy of 71.26% on the Berlin dataset, which is better than other state-of-the-art approaches.
gMeta: Template-based Regular Expression Generation over Noisy Examples

Regular expression is a technology widely used in software develop-ment for extracting textual data, validating the structure of textual documents, or formatting data. Existing regular expression generation works always assumes that the examples are faultless, however, in real industrial scenarios, this assumption does not fully hold. In this paper, we present a simple but effective templated-based approach to generate regular expressions over noisy examples. Specifically, we design an abstract data form (namely, MetaParam) to approximately describe and cluster the input examples. Then, we propose a practical dynamic thresholding scheme to filter out anomalous examples. Finally, we design a template-based regular expression generation algorithm, which is efficient, interpretable and extensible. We performed an experimental evaluation on two different extraction tasks applied to realworld datasets and obtained promising results in terms of precision. Moreover, gMeta achieves excellent results in real industrial scenarios.
Transport Models for Wave Propagation in Scattering Media with Nonlinear Absorption

This work considers the propagation of high-frequency waves in highly-scattering media where physical absorption of a nonlinear nature occurs. Using the classical tools of the Wigner transform and multiscale analysis, we derive semilinear radiative transport models for the phase-space intensity and the diffusive limits of such transport models. As an application, we consider an inverse problem for the semilinear transport equation, where we reconstruct the absorption coefficients of the equation from a functional of its solution. We obtain a uniqueness result on the inverse problem.
Online Convex Optimization With Long-Term Constraints for Predictable Sequences

In this letter, we investigate the framework of Online Convex Optimization (OCO) for online learning. OCO offers a very powerful online learning framework for many applications. In this context, we study a specific framework of OCO called OCO with long-term constraints. Long-term constraints are introduced typically as an alternative to reduce the complexity of projection at every update step in online optimization. While many algorithmic advances have been made towards online optimization with long-term constraints, these algorithms typically assume that the sequence of cost functions over a certain $T$ finite steps that determine the cost to the online learner are adversarially generated. In many circumstances, the sequence of cost functions may not be unrelated, and thus predictable from those observed till a point of time. In this letter, we study the setting where the sequences are predictable. We present a novel algorithm for online optimization with long-term constraints that can leverage such predictability for linear cost functions. We show that, with a predictor that can supply the gradient information of the next function in the sequence, our algorithm can achieve an overall regret and constraint violation rate that is strictly less than the rate that is achievable without prediction.