Miracle-dz/UniFAR-FTUs huggingface dataset:
CluCDD: Contrastive Dialogue Disentanglement Via Clustering

A huge number of multi-participant dialogues happen online every day, which leads to difficulty in understanding the nature of dialogue dynamics for both humans and machines. Dialogue disentanglement aims at separating an entangled dialogue into detached sessions, thus increasing the readability of long disordered dialogue. Previous studies mainly focus on message-pair classification and clustering in two-step methods, which cannot guarantee the whole clustering performance in a dialogue. To address this challenge, we propose a simple yet effective model named CluCDD, which aggregates utterances by contrastive learning. More specifically, our model pulls utterances in the same session together and pushes away utterances in different ones. Then a clustering method is adopted to generate predicted clustering labels. Comprehensive experiments conducted on the Movie Dialogue dataset and IRC dataset demonstrate that our model achieves a new state-of-the-art result 1.1Code is available at https://github.com/gaojingsheng/CluCDD
The role of baryons in self-interacting dark matter mergers

Mergers of galaxy clusters are promising probes of dark matter (DM) physics. For example, an offset between the DM component and the galaxy distribution can constrain DM self-interactions. We investigate the role of the intracluster medium (ICM) and its influence on DM–galaxy offsets in self-interacting dark matter models. To this end, we employ Smoothed Particle Hydrodynamics + N-body simulations to study idealized setups of equal- and unequal-mass mergers with head-on collisions. Our simulations show that the ICM hardly affects the offsets arising shortly after the first pericentre passage compared to DM-only simulations. But later on, e.g. at the first apocentre, the offsets can be amplified by the presence of the ICM. Furthermore, we find that cross-sections small enough not to be excluded by measurements of the core sizes of relaxed galaxy clusters have a chance to produce observable offsets. We found that different DM models affect the DM distribution and also the galaxy and ICM distribution, including its temperature. Potentially, the position of the shock fronts, combined with the brightest cluster galaxies, provides further clues to the properties of DM. Overall our results demonstrate that mergers of galaxy clusters at stages about the first apocentre passage could be more interesting in terms of DM physics than those shortly after the first pericentre passage. This may motivate further studies of mergers at later evolutionary stages.
Probing higher-spin particles with gravitational waves from compact binary inspirals

Under the framework of gravitational effective field theory, we propose a theory agnostic strategy of searching for higher-spin particles with gravitational waves from compact binary inspirals. Using this strategy, we analyze gravitational wave signals from the binary black hole merger events GW151226 and GW170608, as well as the binary neutron star merger event GW170817. We find that the existence of higher-spin particles with mass ranged from $10^{-12} {\rm eV}$ to $10^{-11} {\rm eV}$ is strongly disfavored by these events unless the particles precisely combine within a supersymmetric supermultiplet. We argue that the gravitational effective field theory also provides a framework to search for signals beyond GR from other GW observations, such as extreme-mass-ratio-inspirals.
Learning Density-Based Correlated Equilibria for Markov Games

Correlated Equilibrium (CE) is a well-established solution concept that captures coordination among agents and enjoys good algorithmic properties. In real-world multi-agent systems, in addition to being in an equilibrium, agents' policies are often expected to meet requirements with respect to safety, and fairness. Such additional requirements can often be expressed in terms of the state density which measures the state-visitation frequencies during the course of a game. However, existing CE notions or CE-finding approaches cannot explicitly specify a CE with particular properties concerning state density; they do so implicitly by either modifying reward functions or using value functions as the selection criteria. The resulting CE may thus not fully fulfil the state-density requirements. In this paper, we propose Density-Based Correlated Equilibria (DBCE), a new notion of CE that explicitly takes state density as selection criterion. Concretely, we instantiate DBCE by specifying different state-density requirements motivated by real-world applications. To compute DBCE, we put forward the Density Based Correlated Policy Iteration algorithm for the underlying control problem. We perform experiments on various games where results demonstrate the advantage of our CE-finding approach over existing methods in scenarios with state-density concerns.
Commonsense Reasoning for Conversational AI: A Survey of the State of the Art

Large, transformer-based pretrained language models like BERT, GPT, and T5 have demonstrated a deep understanding of contextual semantics and language syntax. Their success has enabled significant advances in conversational AI, including the development of open-dialogue systems capable of coherent, salient conversations which can answer questions, chat casually, and complete tasks. However, state-of-the-art models still struggle with tasks that involve higher levels of reasoning - including commonsense reasoning that humans find trivial. This paper presents a survey of recent conversational AI research focused on commonsense reasoning. The paper lists relevant training datasets and describes the primary approaches to include commonsense in conversational AI. The paper also discusses benchmarks used for evaluating commonsense in conversational AI problems. Finally, the paper presents preliminary observations of the limited commonsense capabilities of two state-of-the-art open dialogue models, BlenderBot3 and LaMDA, and its negative effect on natural interactions. These observations further motivate research on commonsense reasoning in conversational AI.
C1-smooth isogeometric spline functions of general degree over planar mixed meshes: The case of two quadratic mesh elements

Splines over triangulations and splines over quadrangulations (tensor product splines) are two common ways to extend bivariate polynomials to splines. However, combination of both approaches leads to splines defined over mixed triangle and quadrilateral meshes using the isogeometric approach. Mixed meshes are especially useful for representing complicated geometries obtained e.g. from trimming. As (bi-)linearly parameterized mesh elements are not flexible enough to cover smooth domains, we focus in this work on the case of planar mixed meshes parameterized by (bi-)quadratic geometry mappings. In particular we study in detail the space of $C^1$-smooth isogeometric spline functions of general polynomial degree over two such mixed mesh elements. We present the theoretical framework to analyze the smoothness conditions over the common interface for all possible configurations of mesh elements. This comprises the investigation of the dimension as well as the construction of a basis of the corresponding $C^1$-smooth isogeometric spline space over the domain described by two elements. Several examples of interest are presented in detail.
Fossil image identification using deep learning ensembles of data augmented multiviews

Identification of fossil species is crucial to evolutionary studies. Recent advances from deep learning have shown promising prospects in fossil image identification. However, the quantity and quality of labelled fossil images are often limited due to fossil preservation, conditioned sampling and expensive and inconsistent label annotation by domain experts, which pose great challenges to training deep learning‐based image classification models. To address these challenges, we follow the idea of the wisdom of crowds and propose a multiview ensemble framework, which collects Original (O), Grey (G) and Skeleton (S) views of each fossil image reflecting its different characteristics to train multiple base models, and then makes the final decision via soft voting. Experiments on the largest fusulinid dataset with 2400 images show that the proposed OGS consistently outperforms baselines (using a single model for each view), and obtains superior or comparable performance compared to OOO (using three base models for three the same Original views). Besides, as the training data decreases, the proposed framework achieves more gains. While considering the identification consistency estimation with respect to human experts, OGS receives the highest agreement with the original labels of dataset and with the re‐identifications of two human experts. The validation performance provides a quantitative estimation of consistency across different experts and genera. We conclude that the proposed framework can present state‐of‐the‐art performance in the fusulinid fossil identification case study. This framework is designed for general fossil identification and it is expected to see applications to other fossil datasets in future work. Notably, the result, which shows more performance gains as train set size decreases or over a smaller imbalance fossil dataset, suggests the potential application to identify rare fossil images. The proposed framework also demonstrates its potential for assessing and resolving inconsistencies in fossil identification.
Deterministic Nonsmooth Nonconvex Optimization

We study the complexity of optimizing nonsmooth nonconvex Lipschitz functions by producing $(\delta,\epsilon)$-stationary points. Several recent works have presented randomized algorithms that produce such points using $\tilde O(\delta^{-1}\epsilon^{-3})$ first-order oracle calls, independent of the dimension $d$. It has been an open problem as to whether a similar result can be obtained via a deterministic algorithm. We resolve this open problem, showing that randomization is necessary to obtain a dimension-free rate. In particular, we prove a lower bound of $\Omega(d)$ for any deterministic algorithm. Moreover, we show that unlike smooth or convex optimization, access to function values is required for any deterministic algorithm to halt within any finite time. On the other hand, we prove that if the function is even slightly smooth, then the dimension-free rate of $\tilde O(\delta^{-1}\epsilon^{-3})$ can be obtained by a deterministic algorithm with merely a logarithmic dependence on the smoothness parameter. Motivated by these findings, we turn to study the complexity of deterministically smoothing Lipschitz functions. Though there are efficient black-box randomized smoothings, we start by showing that no such deterministic procedure can smooth functions in a meaningful manner, resolving an open question. We then bypass this impossibility result for the structured case of ReLU neural networks. To that end, in a practical white-box setting in which the optimizer is granted access to the network's architecture, we propose a simple, dimension-free, deterministic smoothing that provably preserves $(\delta,\epsilon)$-stationary points. Our method applies to a variety of architectures of arbitrary depth, including ResNets and ConvNets. Combined with our algorithm, this yields the first deterministic dimension-free algorithm for optimizing ReLU networks, circumventing our lower bound.
A structured jet explains the extreme GRB 221009A

Long-duration gamma-ray bursts (GRBs) are powerful cosmic explosions, signaling the death of massive stars. Among them, GRB 221009A is by far the brightest burst ever observed. Because of its enormous energy (Eiso ≈ 1055 erg) and proximity (z ≈ 0.15), GRB 221009A is an exceptionally rare event that pushes the limits of our theories. We present multiwavelength observations covering the first 3 months of its afterglow evolution. The x-ray brightness decays as a power law with slope ≈t−1.66, which is not consistent with standard predictions for jetted emission. We attribute this behavior to a shallow energy profile of the relativistic jet. A similar trend is observed in other energetic GRBs, suggesting that the most extreme explosions may be powered by structured jets launched by a common central engine.
Multi-Task Differential Privacy Under Distribution Skew

We study the problem of multi-task learning under user-level differential privacy, in which $n$ users contribute data to $m$ tasks, each involving a subset of users. One important aspect of the problem, that can significantly impact quality, is the distribution skew among tasks. Certain tasks may have much fewer data samples than others, making them more susceptible to the noise added for privacy. It is natural to ask whether algorithms can adapt to this skew to improve the overall utility. We give a systematic analysis of the problem, by studying how to optimally allocate a user's privacy budget among tasks. We propose a generic algorithm, based on an adaptive reweighting of the empirical loss, and show that when there is task distribution skew, this gives a quantifiable improvement of excess empirical risk. Experimental studies on recommendation problems that exhibit a long tail of small tasks, demonstrate that our methods significantly improve utility, achieving the state of the art on two standard benchmarks.
The Complexity of Graph Exploration Games

The Graph Exploration problem asks a searcher to explore an unknown environment. The environment is modeled as a graph, where the searcher needs to visit each vertex beginning at some vertex $s$. Furthermore, Treasure Hunt problems are a variation of Graph Exploration, in which the searcher needs to find a hidden treasure, which is located at some vertex $t$. In these online problems, any online algorithm performs poorly because it has too little knowledge about the instance to react adequately to the requests of the adversary. Thus, the impact of a priori knowledge is of interest. In graph problems, one form of a priori knowledge is a map of the graph. We survey the graph exploration and treasure hunt problem with an unlabeled map, which is an isomorphic copy of the graph, that is provided to the searcher. We formulate decision variants of both problems by interpreting the online problems as a game between the online algorithm (the searcher) and the adversary. The map, however, is not controllable by the adversary. The question is, whether the searcher is able to explore the graph fully or find the treasure for all possible decisions of the adversary. We prove the PSPACE-completeness of these games, whereby we analyze the variations which ask for the mere existence of a tour through the graph or path to the treasure and the variations that include costs. Additionally, we analyze the complexity of related problems that relax the path constraint, allowing multiple visits of vertices or edges, or have additional constraints, like requiring to visit specific edges.
LabelPrompt: Effective Prompt-based Learning for Relation Classification

Recently, prompt-based learning has gained popularity across many natural language processing (NLP) tasks by reformulating them into a cloze-style format to better align pre-trained language models (PLMs) with downstream tasks. However, applying this approach to relation classification poses unique challenges. Specifically, associating natural language words that fill the masked token with semantic relation labels (\textit{e.g.} \textit{``org:founded\_by}'') is difficult. To address this challenge, this paper presents a novel prompt-based learning method, namely LabelPrompt, for the relation classification task. Motivated by the intuition to ``GIVE MODEL CHOICES! '', we first define additional tokens to represent relation labels, which regard these tokens as the verbaliser with semantic initialisation and explicitly construct them with a prompt template method. Then, to mitigate inconsistency between predicted relations and given entities, we implement an entity-aware module with contrastive learning. Last, we conduct an attention query strategy within the self-attention layer to differentiates prompt tokens and sequence tokens. Together, these strategies enhance the adaptability of prompt-based learning, especially when only small labelled datasets is available. Comprehensive experiments on benchmark datasets demonstrate the superiority of our method, particularly in the few-shot scenario.
Non-perturbative investigation of low eccentricity exterior mean motion resonances

Mean motion resonances are important in the analysis and understanding of the dynamics of planetary systems. While perturbative approaches have been dominant in many previous studies, recent non-perturbative approaches have revealed novel properties in the low eccentricity regime for interior mean motion resonances of Jupiter in the fundamental model of the circular planar restricted three body model. Here we extend the non-perturbative investigation to exterior mean motion resonances in the low eccentricity regime (up to about 0.1) and for perturber mass in the range ∼5 × 10−5 to 1 × 10−3 (in units of the central mass). Our results demonstrate that first order exterior resonances have two branches at low eccentricity as well as low-eccentricity bridges connecting neighboring first order resonances. With increasing perturber mass, higher order resonances dissolve into chaos whereas low order resonances persist with larger widths in their radial extent but smaller azimuthal widths. For low order resonances, we also detect secondary resonances arising from small integer commensurabilities between resonant librations and the synodic frequency. These secondary resonances contribute significantly to generating the chaotic sea that typically occurs near mean motion resonances of higher-mass perturbers.
Stiff piano string modeling: Computational comparison between finite differences and digital waveguide

As is well known, digital waveguides offer a computationally efficient, and physically motivated, means of simulating wave propagation in strings. The method is based on sampling the traveling wave solution to the ideal wave equation and linearly filtering this solution to simulate dispersive effects due to stiffness and frequency‐dependent loss; such digital filters may terminate the waveguide or be embedded along its length. For strings of high stiffness, however, dispersion filters can be difficult to design and expensive to implement. It is shown how high‐quality time‐domain terminating filters may be derived from given frequency‐domain specifications which depend on the model parameters. Particular attention is paid to the problem of phase approximation, which, in the case of high stiffness, is strongly nonlinear. Finally, in the interest of determining the limits of applicability of digital waveguide techniques, we make a comparison with more conventional finite difference schemes, in terms of compu.
Stable-Limit Non-symmetric Macdonald Functions in Type A

We construct and study an explicit simultaneous $\mathscr{Y}$ eigenbasis of Ion and Wu's standard representation of the $^+$stable-limit double affine Hecke algebra for the limit Cherednik operators $\mathscr{Y}_i$. This basis arises as a generalization of Cherednik's non-symmetric Macdonald polynomials of type $GL_n$. We utilize links between $^+$stable-limit double affine Hecke algebra theory of Ion and Wu and the double Dyck path algebra of Carlsson and Mellit that arose in their proof of the Shuffle Conjecture. As a consequence, the spectral theory for the limit Cherednik operators is understood.
Lattice piecewise affine approximation of explicit nonlinear model predictive control with application to trajectory tracking of mobile robot

To promote the widespread use of mobile robots in diverse fields, the performance of trajectory tracking must be ensured. To address the constraints and nonlinear features associated with mobile robot systems, nonlinear model predictive control (MPC) is applied to realize trajectory tracking of mobile robots. Specifically, to alleviate the online computational complexity of nonlinear MPC, this paper devises a lattice piecewise affine (PWA) approximation method that can approximate both the nonlinear system and control law of explicit nonlinear MPC. The kinematic model of the mobile robot is successively linearized along the trajectory to obtain a linear time‐varying description of the system, which is then expressed using a lattice PWA model. Subsequently, the nonlinear MPC problem can be transformed into a series of linear MPC problems. Furthermore, to reduce the complexity of online calculation of multiple linear MPC problems, the optimal solution of the linear MPCs is approximated by using the lattice PWA models. That is, for different sampling states, the optimal control inputs are obtained offline, and lattice PWA approximations are constructed for the state control pairs. Simulations are performed to evaluate the performance of the method in comparison with the state‐of‐the‐art methods, and the results show that the method has a higher online computing speed and can decrease the offline computing time without significantly increasing the tracking error.
From Graph Generation to Graph Classification

This note describes a new approach to classifying graphs that leverages graph generative models (GGM). Assuming a GGM that defines a joint probability distribution over graphs and their class labels, I derive classification formulas for the probability of a class label given a graph. A new conditional ELBO can be used to train a generative graph auto-encoder model for discrimination. While leveraging generative models for classification has been well explored for non-relational i.i. d. data, to our knowledge it is a novel approach to graph classification.
DETECÇÃO AUTOMÁTICA DE CONTORNOS DE EDIFICAÇÕES UTILIZANDO IMAGEM GERADA POR CÂMARA DIGITAL DE PEQUENO FORMATO E DADOS LIDAR

This work presents a methodology to automatically extract 2D building contours from the integration between a small format digital camera image and laser scanning raw data. The external image orientation and internal camera orientation parameters are assumed to be known. As a common space to integrate information from both sources it is used the image space without distortion. To remove systematic errors in the original image, the Newton-Raphson iterative method for simultaneous equation is used in a resampling process to this space. At this step, all the pixels have color and altitude associated information. The RGB color values are transformed to (almost isotropic) CIELUV color space and the image is segmented according to these values, by mean shift algorithm. The forward steps filter the gotten segments, using colors (to remove green vegetation), medium altitude of a region comparing it with the altitude of its neighbours (to preserve high regions), and polygonal simplification degree (Douglas-Peucker), attempting to detect linear features. Several experiments realized over a Centro Politecnico - UFPR area, in Curitiba/PR, are discussed, with good results for isolated buildings.
Meeting the Needs of Low-Resource Languages: The Value of Automatic Alignments via Pretrained Models

Large multilingual models have inspired a new class of word alignment methods, which work well for the model’s pretraining languages. However, the languages most in need of automatic alignment are low-resource and, thus, not typically included in the pretraining data. In this work, we ask: How do modern aligners perform on unseen languages, and are they better than traditional methods? We contribute gold-standard alignments for Bribri–Spanish, Guarani–Spanish, Quechua–Spanish, and Shipibo-Konibo–Spanish. With these, we evaluate state-of-the-art aligners with and without model adaptation to the target language. Finally, we also evaluate the resulting alignments extrinsically through two downstream tasks: named entity recognition and part-of-speech tagging. We find that although transformer-based methods generally outperform traditional models, the two classes of approach remain competitive with each other.