Miracle-dz/UniFAR-FTUs huggingface dataset:
Shedding Light on Static Partitioning Hypervisors for Arm-based Mixed-Criticality Systems

In this paper, we aim to understand the properties and guarantees of static partitioning hypervisors (SPH) for Arm-based mixed-criticality systems (MCS). To this end, we performed a comprehensive empirical evaluation of popular open-source SPH, i.e., Jailhouse, Xen (Dom0-less), Bao, and seL4 CAmkES VMM, focusing on two key requirements of modern MCS: real-time and safety. The goal of this study is twofold. Firstly, to empower industrial practitioners with hard data to reason about the different trade-offs of SPH. Secondly, we aim to raise awareness of the research and open-source communities to the still open problems in SPH by unveiling new insights regarding lingering weaknesses. All artifacts will be open-sourced to enable independent validation of results and encourage further exploration on SPH.
MT-SNN: Enhance Spiking Neural Network with Multiple Thresholds

Spiking neural networks (SNNs), as a biology-inspired method mimicking the spiking nature of brain neurons, is a promising energy-efficient alternative to the traditional artificial neural networks (ANNs). The energy saving of SNNs is mainly from multiplication free property brought by binarized intermediate activations. In this paper, we proposed a Multiple Threshold (MT) approach to alleviate the precision loss brought by the binarized activations, such that SNNs can reach higher accuracy at fewer steps. We evaluate the approach on CIFAR10, CIFAR100 and DVS-CIFAR10, and demonstrate that MT can promote SNNs extensively, especially at early steps. For example, With MT, Parametric-Leaky-Integrate-Fire(PLIF) based VGG net can even outperform the ANN counterpart with 1 step.
Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models

Simple Summary Artificial intelligence (AI) based on deep neural networks (DNNs) has demonstrated great performance in computer vision. However, their clinical application in the diagnosis and prognosis of cancer using medical imaging has been limited. Not knowing the AI’s decision-making process (interpretability) presents a major obstacle in AI medical applications. To this end, we studied and propose the integration of DNN-derived biomarkers and expert-derived radiomics in interpretable ConRad models. ConRad models achieved great performance for malignancy classification while maintaining inherent interpretability. Without interpretability, a black box classifier such as end-to-end DNNs may harbor critical failure modes that are unknown and unknowable. Abstract Despite the unprecedented performance of deep neural networks (DNNs) in computer vision, their clinical application in the diagnosis and prognosis of cancer using medical imaging has been limited. One of the critical challenges for integrating diagnostic DNNs into radiological and oncological applications is their lack of interpretability, preventing clinicians from understanding the model predictions. Therefore, we studied and propose the integration of expert-derived radiomics and DNN-predicted biomarkers in interpretable classifiers, which we refer to as ConRad, for computerized tomography (CT) scans of lung cancer. Importantly, the tumor biomarkers can be predicted from a concept bottleneck model (CBM) such that once trained, our ConRad models do not require labor-intensive and time-consuming biomarkers. In our evaluation and practical application, the only input to ConRad is a segmented CT scan. The proposed model was compared to convolutional neural networks (CNNs) which act as a black box classifier. We further investigated and evaluated all combinations of radiomics, predicted biomarkers and CNN features in five different classifiers. We found the ConRad models using nonlinear SVM and the logistic regression with the Lasso outperformed the others in five-fold cross-validation, with the interpretability of ConRad being its primary advantage. The Lasso is used for feature selection, which substantially reduces the number of nonzero weights while increasing the accuracy. Overall, the proposed ConRad model combines CBM-derived biomarkers and radiomics features in an interpretable ML model which demonstrates excellent performance for lung nodule malignancy classification.
2023 Quadratic reduction of approximation error of pure quantum states by optimal probabilistic state synthesis

When preparing a pure state with a quantum circuit, there is an inevitable coherent error since each unitary gate suﬀers from the discretized coherent error due to fault-tolerant implementation. A recently proposed approach called probabilistic state synthesis, where the circuit is probabilistically sampled to turn such coherent errors into incoherent ones, is able to reduce the order of the approximation error compared to conventional deterministic synthesis. In this paper, we demonstrate that the optimal probabilistic synthesis quadratically reduces the approximation error with respect to the trace distance. We also show that a deterministic synthesis algorithm can be eﬃciently converted into a probabilistic one to achieve quadratic error reduction. To estimate how the error reduction aﬀects the circuit size, we show that probabilistic encoding asymptotically halves the length of the classical bit string required to approximately encode a pure state. As a byproduct of our technique, we provide a proof for conjectures about the minimum trace distance between an entangled state and the set of separable states and alternate proof about a recently identiﬁed coincidence between an entanglement measure and a coherence measure.
Existence of Traveling Waves of Lotka Volterra Type Models with Delayed Diffusion Term and Partial Quasimonotonicity

This paper is concerned with the existence of traveling wave solutions for diffusive two-species Lotka-Volterra systems with delay in both the reaction and diffusion terms without monotonicity. We extend the partial or cross monotone iteration method to systems that satisfy the partial quasi-monotone condition via construction appropriate upper and lower solutions. This is done by using Schauder's fixed point theorem.
SPDF: Sparse Pre-training and Dense Fine-tuning for Large Language Models

The pre-training and fine-tuning paradigm has contributed to a number of breakthroughs in Natural Language Processing (NLP). Instead of directly training on a downstream task, language models are first pre-trained on large datasets with cross-domain knowledge (e.g., Pile, MassiveText, etc.) and then fine-tuned on task-specific data (e.g., natural language generation, text summarization, etc.). Scaling the model and dataset size has helped improve the performance of LLMs, but unfortunately, this also lead to highly prohibitive computational costs. Pre-training LLMs often require orders of magnitude more FLOPs than fine-tuning and the model capacity often remains the same between the two phases. To achieve training efficiency w.r. t training FLOPs, we propose to decouple the model capacity between the two phases and introduce Sparse Pre-training and Dense Fine-tuning (SPDF). In this work, we show the benefits of using unstructured weight sparsity to train only a subset of weights during pre-training (Sparse Pre-training) and then recover the representational capacity by allowing the zeroed weights to learn (Dense Fine-tuning). We demonstrate that we can induce up to 75% sparsity into a 1.3B parameter GPT-3 XL model resulting in a 2.5x reduction in pre-training FLOPs, without a significant loss in accuracy on the downstream tasks relative to the dense baseline. By rigorously evaluating multiple downstream tasks, we also establish a relationship between sparsity, task complexity and dataset size. Our work presents a promising direction to train large GPT models at a fraction of the training FLOPs using weight sparsity, while retaining the benefits of pre-trained textual representations for downstream tasks.
An importance sampling method for Feldman-Cousins confidence intervals

In various high-energy physics contexts, such as neutrino-oscillation experiments, several assumptions underlying the typical asymptotic confidence interval construction are violated, such that one has to resort to computationally expensive methods like the Feldman-Cousins method for obtaining confidence intervals with proper statistical coverage. By construction, the computation of intervals at high confidence levels requires fitting millions or billions of pseudo-experiments, while wasting most of the computational cost on overly precise intervals at low confidence levels. In this work, a simple importance sampling method is introduced which reuses pseudo-experiments produced for all tested parameter values in a single mixture distribution. This results in a significant error reduction on the estimated critical values, especially at high confidence levels, and simultaneously yields a correct interpolation of these critical values between the parameter values at which the pseudo-experiments were produced. The theoretically calculated performance is demonstrated numerically using a simple example from the analysis of neutrino oscillations. The relationship to similar techniques applied in statistical mechanics and $p$-value computations is discussed.
Optimization-Based Constrained Funnel Synthesis for Systems With Lipschitz Nonlinearities via Numerical Optimal Control

This letter presents a funnel synthesis algorithm for computing controlled invariant sets and feedback control gains around a given nominal trajectory for dynamical systems with locally Lipschitz nonlinearities and bounded disturbances. The resulting funnel synthesis problem involves a differential linear matrix inequality (DLMI) whose solution satisfies a Lyapunov condition that implies invariance and attractivity properties. Due to these properties, the proposed method can balance maximization of initial invariant funnel size, i.e., size of the funnel entry, and minimization of the size of the attractive funnel for attenuating the effect of disturbance. To solve the resulting funnel synthesis problem with the DLMI as constraints, we employ a numerical optimal control approach that uses a multiple shooting method to convert the problem into a finite dimensional semidefinite programming problem. This framework does not require piecewise linear system matrices and funnel parameters, which is typically assumed in recent related work. We illustrate the proposed funnel synthesis method with a numerical example.
Lasing in a ZnO waveguide: Clear evidence of polaritonic gain obtained by monitoring the continuous exciton screening

The stimulated emission of exciton-polaritons was proposed as a means of lowering the lasing threshold because it does not require the dissociation of excitons to obtain an electron-hole plasma, as in a classical semiconductor laser based on population inversion. In this work we propose a method to prove unambiguously the polaritonic nature of lasing by combining experimental measurements with a model accounting for the permittivity change as a function of the carrier density. To do so we use angle resolved photoluminescence to observe the lasing at cryogenic temperature from a polariton mode in a zinc oxide waveguide structure, and to monitor the continuous shift of the polaritonic dispersion towards a photonic dispersion as the optical intensity of the pump is increased (up to 20 times the one at threshold). This shift is reproduced thanks to a model taking into account the reduction of the oscillator strength and the renormalization of the bandgap due to the screening of the electrostatic interaction between electrons and holes. Furthermore, the measurement of the carriers lifetime at optical intensities in the order of those at which the polariton lasing occurs enables us to estimate the carrier density, confirming it is lower than the corresponding Mott density for zinc oxide reported in the literature.
Design and comparison of LQR and a novel robust backstepping controller for supercavitating vehicles

Traditional underwater vehicles are limited in speed due to dramatic friction drag on the hull. Supercavitating vehicles exploit supercavitation as a means to reduce drag and increase their underwater speed. Compared with fully wetted vehicles, the non-linearity in the modelling of cavitator, fin and in particular the planing force make the control design of supercavitating vehicles more challenging. Dominant non-linearities associated with planing force are taken into account in the model of supercavitating vehicles in this paper. Two controllers are proposed to realize stable system dynamics and tracking responses, a linear quadratic regulator (LQR) control scheme and a robust backstepping control (RBC) scheme. The proposed backstepping procedure, in association with integral filters technique, exploits the possibility of avoiding the overparameterization problem existing in the classical backstepping process. In particular, the achieved stability is robust to modelling errors in supercavitating vehicles. Compared with the LQR control scheme, the RBC scheme is seen to increase the robustness with saturation compensation algorithm, which can be useful for avoiding actuator saturation in magnitude.
Instance-dependent Sample Complexity Bounds for Zero-sum Matrix Games

We study the sample complexity of identifying an approximate equilibrium for two-player zero-sum $n\times 2$ matrix games. That is, in a sequence of repeated game plays, how many rounds must the two players play before reaching an approximate equilibrium (e.g., Nash)? We derive instance-dependent bounds that define an ordering over game matrices that captures the intuition that the dynamics of some games converge faster than others. Specifically, we consider a stochastic observation model such that when the two players choose actions $i$ and $j$, respectively, they both observe each other's played actions and a stochastic observation $X_{ij}$ such that $\mathbb E[ X_{ij}] = A_{ij}$. To our knowledge, our work is the first case of instance-dependent lower bounds on the number of rounds the players must play before reaching an approximate equilibrium in the sense that the number of rounds depends on the specific properties of the game matrix $A$ as well as the desired accuracy. We also prove a converse statement: there exist player strategies that achieve this lower bound.
Towards Diverse Binary Segmentation via A Simple yet General Gated Network

In many binary segmentation tasks, most CNNs-based methods use a U-shape encoder-decoder network as their basic structure. They ignore two key problems when the encoder exchanges information with the decoder: one is the lack of interference control mechanism between them, the other is without considering the disparity of the contributions from different encoder levels. In this work, we propose a simple yet general gated network (GateNet) to tackle them all at once. With the help of multi-level gate units, the valuable context information from the encoder can be selectively transmitted to the decoder. In addition, we design a gated dual branch structure to build the cooperation among the features of different levels and improve the discrimination ability of the network. Furthermore, we introduce a ``Fold'' operation to improve the atrous convolution and form a novel folded atrous convolution, which can be flexibly embedded in ASPP or DenseASPP to accurately localize foreground objects of various scales. GateNet can be easily generalized to many binary segmentation tasks, including general and specific object segmentation and multi-modal segmentation. Without bells and whistles, our network consistently performs favorably against the state-of-the-art methods under 10 metrics on 33 datasets of 10 binary segmentation tasks.
Point-MA2E: Masked and Afﬁne Transformed AutoEncoder for Self-supervised Point Cloud Learning

Masked modeling has demonstrated its effectiveness in self-supervised point cloud learning by reconstructing the complete point cloud from its masked counterpart. Considering that masking only corrupts partial points of the input, in this paper, we promote the afﬁne transformation, which corrupts all input points with certain rules, to complement the popular masking strategy, leading to the Masked and Afﬁne transformed AutoEncoder for point cloud learning (Point-MA2E). Generally, we corrupt the point cloud with afﬁne transformation and masking as input and learn an encoder-decoder model to reconstruct the original point cloud from its corrupted version. Various point cloud encoders are explored in this study. For non-Transformer encoders, we follow the common practice to reconstruct the uncorrupted point cloud directly. For Transformer-based encoders, we decompose the reconstruction of the complete point cloud into the reconstructions of detailed lo-cal patches and rough global shape, facilitating the pre-training efﬁciency. We perform in-depth analyses of the proposed components and validate the effectiveness of Point-MA2E with extensive experiments on object classiﬁcation, few-shot learning, robustness testing, part segmentation, and 3D object detection. The source code will be made publicly available.
Inexact Newton Methods for Solving Generalized Equations on Riemannian Manifolds

The local convergence of an inexact Newton method is studied for solving generalized equations on Riemannian manifolds by using the metric regularity property which is explored as well. Under suitable conditions and without any additional geometric assumptions, local convergence results with linear and quadratic rate and a semi-local convergence result are obtained for the proposed method. Finally, the theory can be applied to problems of finding a singularity of the sum of two vector fields.
Semantic 3D Scene Segmentation for Robotic Assembly Process Execution

Adapting robot programmes to changes in the environment is a well-known industry problem, and it is the reason why many tedious tasks are not automated in small and medium-sized enterprises (SMEs). A semantic world model of a robot's previously unknown environment created from point clouds is one way for these companies to automate assembly tasks that are typically performed by humans. The semantic segmentation of point clouds for robot manipulators or cobots in industrial environments has received little attention due to a lack of suitable datasets. This paper describes a pipeline for creating synthetic point clouds for specific use cases in order to train a model for point cloud semantic segmentation. We show that models trained with our data achieve high per-class accuracy (>90%) for semantic point cloud segmentation on unseen real-world data. Our approach is applicable not only to the 3D camera used in training data generation but also to other depth cameras based on different technologies. The application tested in this work is a industry-related peg-in-the-hole process. With our approach the necessity of user assistance during a robot's commissioning can be reduced to a minimum.
GRB 221009A: An Ordinary Nearby GRB with Extraordinary Observational Properties

The gamma-ray burst GRB 221009A, known as the “brightest of all time,” is the closest energetic burst detected so far, with an energy of E γ,iso ∼ 1055 erg. This study aims to assess its compatibility with known GRB energy and luminosity distributions. Our analysis indicates that the energy/luminosity function of GRBs is consistent across various redshift intervals, and that the inclusion of GRB 221009A does not significantly impact the function at low redshifts. Additionally, our evaluation of the best-fitting result of the entire GRB sample suggests that the expected number of GRBs with energy greater than 1055 erg at a low redshift is 0.2, so that the emergence of GRB 221009A is consistent with expected energy/luminosity functions within ∼2σ Poisson fluctuation error, still adhering to the principles of small number statistics. Furthermore, we find that GRB 221009A and other energetic bursts, defined as E γ,iso ≳ 1054 erg, exhibit no significant differences in terms of distributions of T 90, minimum timescale, Amati relation, E γ,iso–E X,iso relation, L γ,iso–Γ0 relation, E γ,iso–Γ0 relation, L γ,iso–E p,i–Γ0 relation, and host galaxy properties, compared to normal long GRBs. This suggests that energetic GRBs (including GRB 221009A) and other long GRBs likely have similar progenitor systems and undergo similar energy dissipation and radiation processes. The generation of energetic GRBs may be due to more extreme central engine properties or, more likely, a rarer viewing configuration of a quasi-universal structured jet.
Graph Neural Rough Differential Equations for Traffic Forecasting

Traffic forecasting is one of the most popular spatio-temporal tasks in the field of machine learning. A prevalent approach in the field is to combine graph convolutional networks and recurrent neural networks for the spatio-temporal processing. There has been fierce competition and many novel methods have been proposed. In this article, we present the method of spatio-temporal graph neural rough differential equation (STG-NRDE). Neural rough differential equations (NRDEs) are a breakthrough concept for processing time-series data. Their main concept is to use the log-signature transform to convert a time-series sample into a relatively shorter series of feature vectors. We extend the concept and design two NRDEs: one for the temporal processing and the other for the spatial processing. After that, we combine them into a single framework. We conduct experiments with 6 benchmark datasets and 27 baselines. STG-NRDE shows the best accuracy in all cases, outperforming all those 27 baselines by non-trivial margins.
Local-to-Global Panorama Inpainting for Locale-Aware Indoor Lighting Prediction

Predicting panoramic indoor lighting from a single perspective image is a fundamental but highly ill-posed problem in computer vision and graphics. To achieve locale-aware and robust prediction, this problem can be decomposed into three sub-tasks: depth-based image warping, panorama inpainting and high-dynamic-range (HDR) reconstruction, among which the success of panorama inpainting plays a key role. Recent methods mostly rely on convolutional neural networks (CNNs) to fill the missing contents in the warped panorama. However, they usually achieve suboptimal performance since the missing contents occupy a very large portion in the panoramic space while CNNs are plagued by limited receptive fields. The spatially-varying distortion in the spherical signals further increases the difficulty for conventional CNNs. To address these issues, we propose a local-to-global strategy for large-scale panorama inpainting. In our method, a depth-guided local inpainting is first applied on the warped panorama to fill small but dense holes. Then, a transformer-based network, dubbed PanoTransformer, is designed to hallucinate reasonable global structures in the large holes. To avoid distortion, we further employ cubemap projection in our design of PanoTransformer. The high-quality panorama recovered at any locale helps us to capture spatially-varying indoor illumination with physically-plausible global structures and fine details.