Miracle-dz/UniFAR-FTUs huggingface dataset:
Small but Mighty: Enhancing 3D Point Clouds Semantic Segmentation with U-Next Framework

We study the problem of semantic segmentation of large-scale 3D point clouds. In recent years, significant research efforts have been directed toward local feature aggregation, improved loss functions and sampling strategies. While the fundamental framework of point cloud semantic segmentation has been largely overlooked, with most existing approaches rely on the U-Net architecture by default. In this paper, we propose U-Next, a small but mighty framework designed for point cloud semantic segmentation. The key to this framework is to learn multi-scale hierarchical representations from semantically similar feature maps. Specifically, we build our U-Next by stacking multiple U-Net $L^1$ codecs in a nested and densely arranged manner to minimize the semantic gap, while simultaneously fusing the feature maps across scales to effectively recover the fine-grained details. We also devised a multi-level deep supervision mechanism to further smooth gradient propagation and facilitate network optimization. Extensive experiments conducted on three large-scale benchmarks including S3DIS, Toronto3D, and SensatUrban demonstrate the superiority and the effectiveness of the proposed U-Next architecture. Our U-Next architecture shows consistent and visible performance improvements across different tasks and baseline models, indicating its great potential to serve as a general framework for future research.
H IGH P ROBABILITY AND R ISK -A VERSE G UARANTEES FOR S TOCHASTIC S ADDLE P OINT P ROBLEMS

We consider strongly-convex-strongly-concave (SCSC) saddle point (SP) problems which frequently arise in many applications from distributionally robust learning to game theory and fairness in machine learning. We focus on the recently developed stochastic accelerated primal-dual algorithm (SAPD), which admits optimal complexity in several settings as an accelerated algorithm. We provide high probability guarantees for convergence to a neighborhood of the saddle point that reﬂects accelerated convergence behavior. We also provide an analytical formula for the limiting covariance matrix of the iterates for a class of SCSC quadratic problems where the gradient noise is additive and Gaussian. This allows us to develop lower bounds for this class of quadratic problems which show that our analysis is tight in terms of the high probability bound dependency to the parameters. We also provide a risk-averse convergence analysis characterizing “Conditional Value at Risk” and the “Entropic Value at Risk” of the distance to the saddle point, highlighting the trade-offs between the bias and the risk associated with an approximate solution.
LIPSFUS: A neuromorphic dataset for audio-visual sensory fusion of lip reading

This paper presents a sensory fusion neuromorphic dataset collected with precise temporal synchronization using a set of Address-Event-Representation sensors and tools. The target application is the lip reading of several keywords for different machine learning applications, such as digits, robotic commands, and auxiliary rich phonetic short words. The dataset is enlarged with a spiking version of an audio-visual lip reading dataset collected with frame-based cameras. LIPSFUS is publicly available and it has been validated with a deep learning architecture for audio and visual classification. It is intended for sensory fusion architectures based on both artificial and spiking neural network algorithms.
Clinical Significance of Venous Thromboembolism in Patients with Advanced Cholangiocarcinoma

Background/Aims Patients with active cancer frequently develop venous thromboembolism (VTE). However, there is little data about VTE in patients with advanced cholangiocarcinoma (CCA). Therefore, we investigated the clinical significance of VTE in patients with advanced CCA. Methods We analyzed the data of a total of 332 unresectable CCA patients diagnosed between 2010 and 2020 in this retrospective study. We investigated the incidence and risk factors for VTE, and its effect on survival in patients with advanced CCA. Results During a median follow-up of 11.6 months, 118 patients (35.5%) developed VTE. The cumulative incidence of VTE was 22.4% (95% confidence interval [CI], 0.18 to 0.27) at 3 months and 32.8% (95% CI, 0.27 to 0.38) at 12 months. Major vessel invasion was an independent risk factor for VTE (hazard ratio, 2.88; 95% CI, 1.92 to 4.31; p<0.001). Patients who developed VTE during follow-up had shorter overall survival than patients who did not (11.50 months vs 15.83 months, p=0.005). In multivariable analysis, VTE (hazard ratio, 1.58; 95% CI, 1.23 to 2.02; p<0.001) was associated with poor overall survival. Conclusions Major vessel invasion is related to the occurrence of VTE in advanced CCA. The development of VTE significantly decreases the overall survival and is an important unfavorable prognostic factor for survival.
An End-to-End Human Simulator for Task-Oriented Multimodal Human-Robot Collaboration

This paper proposes a neural network-based user simulator that can provide a multimodal interactive environment for training Reinforcement Learning (RL) agents in collaborative tasks involving multiple modes of communication. The simulator is trained on the existing ELDERLY-AT-HOME corpus and accommodates multiple modalities such as language, pointing gestures, and haptic-ostensive actions. The paper also presents a novel multimodal data augmentation approach, which addresses the challenge of using a limited dataset due to the expensive and time-consuming nature of collecting human demonstrations. Overall, the study highlights the potential for using RL and multimodal user simulators in developing and improving domestic assistive robots.
Model-Agnostic Reachability Analysis on Deep Neural Networks

Verification plays an essential role in the formal analysis of safety-critical systems. Most current verification methods have specific requirements when working on Deep Neural Networks (DNNs). They either target one particular network category, e.g., Feedforward Neural Networks (FNNs), or networks with specific activation functions, e.g., RdLU. In this paper, we develop a model-agnostic verification framework, called DeepAgn, and show that it can be applied to FNNs, Recurrent Neural Networks (RNNs), or a mixture of both. Under the assumption of Lipschitz continuity, DeepAgn analyses the reachability of DNNs based on a novel optimisation scheme with a global convergence guarantee. It does not require access to the network's internal structures, such as layers and parameters. Through reachability analysis, DeepAgn can tackle several well-known robustness problems, including computing the maximum safe radius for a given input, and generating the ground-truth adversarial examples. We also empirically demonstrate DeepAgn's superior capability and efficiency in handling a broader class of deep neural networks, including both FNNs, and RNNs with very deep layers and millions of neurons, than other state-of-the-art verification approaches.
Dsfer-Net: A Deep Supervision and Feature Retrieval Network for Bitemporal Change Detection Using Modern Hopfield Networks

Change detection, as an important application for high-resolution remote sensing images, aims to monitor and analyze changes in the land surface over time. With the rapid growth in the quantity of high-resolution remote sensing data and the complexity of texture features, a number of quantitative deep learning-based methods have been proposed. Although these methods outperform traditional change detection methods by extracting deep features and combining spatial-temporal information, reasonable explanations about how deep features work on improving the detection performance are still lacking. In our investigations, we find that modern Hopfield network layers achieve considerable performance in semantic understandings. In this paper, we propose a Deep Supervision and FEature Retrieval network (Dsfer-Net) for bitemporal change detection. Specifically, the highly representative deep features of bitemporal images are jointly extracted through a fully convolutional Siamese network. Based on the sequential geo-information of the bitemporal images, we then design a feature retrieval module to retrieve the difference feature and leverage discriminative information in a deeply supervised manner. We also note that the deeply supervised feature retrieval module gives explainable proofs about the semantic understandings of the proposed network in its deep layers. Finally, this end-to-end network achieves a novel framework by aggregating the retrieved features and feature pairs from different layers. Experiments conducted on three public datasets (LEVIR-CD, WHU-CD, and CDD) confirm the superiority of the proposed Dsfer-Net over other state-of-the-art methods. Code will be available online (https://github.com/ShizhenChang/Dsfer-Net).
Saddle-to-Saddle Dynamics in Diagonal Linear Networks

In this paper we fully describe the trajectory of gradient flow over diagonal linear networks in the limit of vanishing initialisation. We show that the limiting flow successively jumps from a saddle of the training loss to another until reaching the minimum $\ell_1$-norm solution. This saddle-to-saddle dynamics translates to an incremental learning process as each saddle corresponds to the minimiser of the loss constrained to an active set outside of which the coordinates must be zero. We explicitly characterise the visited saddles as well as the jumping times through a recursive algorithm reminiscent of the LARS algorithm used for computing the Lasso path. Our proof leverages a convenient arc-length time-reparametrisation which enables to keep track of the heteroclinic transitions between the jumps. Our analysis requires negligible assumptions on the data, applies to both under and overparametrised settings and covers complex cases where there is no monotonicity of the number of active coordinates. We provide numerical experiments to support our findings.
$p$-numerical semigroup of generalized Fibonacci triples

For a nonnegative integer $p$, we give explicit formulas for the $p$-Frobenius number and the $p$-genus of generalized Fibonacci numerical semigroups. Here, the $p$-numerical semigroup $S_p$ is defined as the set of integers whose nonnegative integral linear combinations of given positive integers $a_1,a_2,\dots,a_k$ are expressed more than $p$ ways. When $p=0$, $S_0$ with the $0$-Frobenius number and the $0$-genus is the original numerical semigroup with the Frobenius number and the genus. In this paper, we consider the $p$-numerical semigroup involving Jacobsthal polynomials, which include Fibonacci numbers as special cases. We can also treat with the Jacobsthal-Lucas polynomials, including Lucas numbers accordingly. One of the applications on the $p$-Hilbert series is mentioned.
Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition

Symbolic regression searches for analytic expressions that accurately describe studied phenomena. The main attraction of this approach is that it returns an interpretable model that can be insightful to users. Historically, the majority of algorithms for symbolic regression have been based on evolutionary algorithms. However, there has been a recent surge of new proposals that instead utilize approaches such as enumeration algorithms, mixed linear integer programming, neural networks, and Bayesian optimization. In order to assess how well these new approaches behave on a set of common challenges often faced in real-world data, we hosted a competition at the 2022 Genetic and Evolutionary Computation Conference consisting of different synthetic and real-world datasets which were blind to entrants. For the real-world track, we assessed interpretability in a realistic way by using a domain expert to judge the trustworthiness of candidate models.We present an in-depth analysis of the results obtained in this competition, discuss current challenges of symbolic regression algorithms and highlight possible improvements for future competitions.
Graphical calculus of spin networks

Graphical techniques provide a very useful practical device for calculations involving the so-called spin network states, which encode the quantum degrees of freedom of spatial geometry in loop quantum gravity. Graphical calculus of SU(2), which has been originally introduced in the literature in order to deal with calculations arising from the coupling of angular momenta in quantum mechanics, can be used as a simple but powerful method for computing the action of various physically interesting operators in the spin network representation. Compared with conventional manipulation of algebraic expressions, calculations in the graphical approach are typically more convenient, concise and visually transparent. The goal of this chapter is to provide an accessible introduction to graphical methods in SU(2) recoupling theory and a brief description of their use as a tool for practical calculations in loop quantum gravity. We introduce the basics of the graphical formalism, and establish the representation of the elementary states and operators of loop quantum gravity in graphical form. Several example calculations are given to illustrate the use of the graphical techniques, including a computation of the matrix elements of a Hamiltonian constraint operator in the spin network basis.
Enhancing Deformable Local Features by Jointly Learning to Detect and Describe Keypoints

Local feature extraction is a standard approach in computer vision for tackling important tasks such as image matching and retrieval. The core assumption of most methods is that images undergo affine transformations, disregarding more complicated effects such as non-rigid deformations. Furthermore, incipient works tailored for non-rigid correspondence still rely on keypoint detectors designed for rigid transformations, hindering performance due to the limitations of the detector. We propose DALF (Deformation-Aware Local Features), a novel deformation-aware network for jointly detecting and describing keypoints, to handle the challenging problem of matching deformable surfaces. All network components work cooperatively through a feature fusion approach that enforces the descriptors' distinctiveness and invariance. Experiments using real deforming objects showcase the superiority of our method, where it delivers 8% improvement in matching scores compared to the previous best results. Our approach also enhances the performance of two real-world applications: deformable object retrieval and non-rigid 3D surface registration. Code for training, inference, and applications are publicly available at verlab.dcc. ufmg.br/descriptors/dalf_cvpr23.
Controllable Fano-type optical response and four-wave mixing via magnetoelastic coupling in an opto-magnomechanical system

We analytically investigate the Fano-type optical response and the four-wave mixing (FWM) process by exploiting the magnetoelasticity of a ferromagnetic material. The deformation of the ferromagnetic material plays the role of mechanical displacement, which is simultaneously coupled to both optical and magnon modes. We report that the magnetostrictively induced displacement leads to realization of Fano profiles in the output field and is effectively well-tuned through adjusting the system parameters, such as effective magnomechanical coupling, magnon detuning, and cavity detuning. It is found that the magnetoelastic interaction also gives rise to the FWM phenomenon. The number of the FWM signals mainly depends upon the effective magnomechanical coupling and the magnon detuning. Moreover, the FWM spectrum exhibits suppressive behavior upon increasing (decreasing) the magnon (cavity) decay rate. The present scheme will open new perspectives in highly sensitive detection and quantum information processing.
On the Impact of Gaslighting on Partially Observed Stochastic Control

Recent years have witnessed a significant increase in cyber crimes and system failures caused by misinformation. Many of these instances can be classified as gaslighting, which involves manipulating the perceptions of others through the use of information. In this paper, we propose a dynamic game-theoretic framework built on a partially observed stochastic control system to study gaslighting. The decision-maker (DM) in the game only accesses partial observations, and she determines the controls by constructing information states that capture her perceptions of the system. The gaslighter in the game influences the system indirectly by designing the observations to manipulate the DM's perceptions and decisions. We analyze the impact of the gaslighter's efforts using robustness analysis of the information states and optimal value to deviations in the observations. A stealthiness constraint is introduced to restrict the power of the gaslighter and to help him stay undetected. We consider approximate feedback Stackelberg equilibrium as the solution concept and estimate the cost of gaslighting.
The Simons Observatory: Differentiating Thermal and Optical Effects in Superconducting Transition-Edge Sensing Bolometers

The Simons Observatory aims to field 70,000 Transition-Edge Sensor (TES) bolometers to measure the Cosmic Microwave Background. With so many detectors, rapid but accurate validation of their properties prior to their integration into telescopes is of particular importance. This paper describes an exploration of a new method to improve the simultaneous characterization of TES thermal parameters and bolometer optical efficiencies without significantly increasing the data collection time. The paper uses a special-purpose data set comprising current-voltage (IV) curves collected from thousands of TES bolometers with a variety of different average bath temperatures and different cold load temperatures. A subset of the bolometers were masked so they received no optical power. The new method fits data from the bath temperature ramp and cold load temperature ramps together as one set instead of fitting each independently. This enables thermal parameter assessment of the unmasked detectors without performing additional cooldowns of the cryostat, halving the time necessary to obtain thermal characterization of all detectors.
PLMEs and Disjunctive Decompositions for Bilevel Optimization

This paper studies bilevel polynomial optimization in which lower level constraining functions depend linearly on lower level variables. We show that such a bilevel program can be reformulated as a disjunctive program using partial Lagrange multiplier expressions (PLMEs). An advantage of this approach is that branch problems of the disjunctive program are easier to solve. In particular, since the PLME can be easily obtained, these branch problems can be efficiently solved by polynomial optimization techniques. Solving each branch problem either returns infeasibility or gives a candidate local or global optimizer for the original bilevel optimization. We give necessary and sufficient conditions for these candidates to be global optimizers, and sufficient conditions for the local optimality. Numerical experiments are also presented to show the efficiency of the method.
Trajectory Control for Differential Drive Mobile Manipulators

Mobile manipulator systems are comprised of a mobile platform with one or more manipulators and are of great interest in a number of applications such as indoor warehouses, mining, construction, forestry etc. We present an approach for computing actuator commands for such systems so that they can follow desired end-effector and platform trajectories without the violation of the nonholonomic constraints of the system in an indoor warehouse environment. We work with the Fetch robot which consists of a 7-DOF manipulator with a differential drive mobile base to validate our method. The major contributions of our project are, writing the dynamics of the system, Trajectory planning for the manipulator and the mobile base, state machine for the pick and place task and the inverse kinematics of the manipulator. Our results indicate that we are able to successfully implement trajectory control on the mobile base and the manipulator of the Fetch robot.
Stable Densities, Fractional Integrals and the Mittag-Leffler Function

This paper combines probability theory and fractional calculus to derive a novel integral representation of the three-parameter Mittag-Leffler function or Prabhakar function, where the three parameters are combinations of four base parameters. The fundamental concept is the Riemann-Liouville fractional integral of the one-sided stable density, conditioned on a scale factor. Integrating with respect to a gamma-distributed scale factor induces a mixture of Riemann-Liouville integrals. A particular combination of four base parameters leads to a representation of the Prabhakar function as a weighted mixture of Riemann-Liouville integrals at different scales. The Prabhakar function constructed in this manner is the Laplace transform of a four-parameter distribution. This general approach gives various known results as special cases (notably, the two-parameter generalised Mittag-Leffler distribution).
TSCI: two stage curvature identification for causal inference with invalid instruments

TSCI implements treatment effect estimation from observational data under invalid instruments in the R statistical computing environment. Existing instrumental variable approaches rely on arguably strong and untestable identification assumptions, which limits their practical application. TSCI does not require the classical instrumental variable identification conditions and is effective even if all instruments are invalid. TSCI implements a two-stage algorithm. In the first stage, machine learning is used to cope with nonlinearities and interactions in the treatment model. In the second stage, a space to capture the instrument violations is selected in a data-adaptive way. These violations are then projected out to estimate the treatment effect.
The Focal Quantal Response Equilibrium

We propose a generalization of Quantal Response Equilibrium (QRE) built on a simple premise: some actions are more focal than others. In our model, which we call the Focal Quantal Response Equilibrium (Focal QRE), each player plays a stochastic version of Nash equilibrium as in the QRE, but some strategies are focal and thus are chosen relatively more frequently than other strategies after accounting for expected utilities. The Focal QRE is able to systemically account for various forms of bounded rationality of players, especially regret-aversion, salience, or limited consideration. The Focal QRE is also useful to explain observed heterogeneity of bounded rationality of players across different games. We show that regret-based focal sets perform relatively well at predicting strategies that are chosen more frequently relative to their expected utilities.