Miracle-dz/UniFAR-FTUs huggingface dataset:
Linear matrix inequality relaxations and its application to data‐driven control design for switched affine systems

The problem of data‐driven control is addressed here in the context of switched affine systems. This class of nonlinear systems is of particular importance when controlling many types of applications in electronic, biology, medicine and so forth. Still in the view of practical applications, providing an accurate model for this class of systems can be a hard task, and it might be more relevant to work on data issued from some trajectories obtained from experiments and to deploy a new branch of tools to stabilize the systems that are compatible with the processed data. Following the recent concept of data‐driven control design, this paper first presents a generic equivalence lemma that shows a matrix constraint based on data, instead of the system parameter. Then, following the concept of robust hybrid limit cycles for uncertain switched affine systems, robust model‐based and then data‐driven control laws are designed based on a Lyapunov approach. The proposed results are then illustrated and evaluated on an academic example.
On the clitic switch in Greek imperatives

ly, we have the same pattern in both Greek and English. Both languages disallow a particular verbal form to co-occur with negation. In the relevant negative context, they switch to another verbal form. The parallelism between Greek and English is generally not noted in the existing accounts of the ban on negative imperatives, which appear to have nothing to say about it. (That is, the existing analyses of the ban on negative imperatives are not readily extendable to the ban on negative indicatives in English.) One exception is Miyoshi (2002), who provides a uniform account of the ban on negative imperatives in Greek and the ban on negative indicatives in English. More precisely, he extends a particular account of the ban on negative indicatives in English to the ban on negative imperatives in Greek. The account in question is probably the oldest surviving analysis of the transformational grammar, namely Chomsky’s (1957) affix hopping analysis of verbal morphology in English, revived recently in Hale and Marantz (1993), Bobaljik (1994, 1995), and Lasnik (1995), and extended to several other pheQRPHQD LQ %RãNRYLü D E ,Q WKH UHFHQW LQVWDQWLDWLRQV WKH PHFKDQLVP RI DIIL[ hopping, often referred to as PF merger (I will use the two terms interchangeably), is treated as a morphophonological rule that involves merger between an affix and its host in PF under adjacency. Merger/affix hopping is blocked by intervening phonologically realized elements, but not by phonologically null elements such as traces and pro. To illustrate how the mechanism works, consider (4a-c), whose structures before PF merger and Do-Support are given in (5). (4) a. John laughed. b. *John not laughed. c. John did not laugh. (5) a. [IP Johni I (ed) [vP ti laugh]] b. [IP Johni I (ed) [NegP not [VP ti laugh]]] Assume that English I is a verbal PF affix, hence must merge with a verbal element in PF under adjacency. The adjacency requirement is not met in (5b) due to the intervening negative head, which blocks PF merger. Do-Support, a last resort operation, then takes place to save the stranded affix, deriving (4c). In (5a), the merger is not blocked since no phonologically realized element intervenes between I and the verb. I then merges with the verb, deriving (4a). The crux of the analysis is that indicatives cannot cooccur with negation in English because the cooccurrence results in a violation of the Stranded Affix Filter, which filters out constructions with stranded affixes. Miyoshi (2002) puts forward the same explanation for the ban on negative imperatives. He proposes that imperatives in languages like Greek contain a
Accurate ignition detection of solid fuel particles using machine learning

In the present work, accurate determination of single-particle ignition is focused on using high-speed optical diagnostics combined with machine learning approaches. Ignition of individual particles in a laminar flow reactor are visualized by simultaneous 10 kHz OH-LIF and DBI measurements. Two coal particle sizes of 90-125{\mu}m and 160-200{\mu}m are investigated in conventional air and oxy-fuel conditions with increasing oxygen concentrations. Ignition delay times are first evaluated with threshold methods, revealing obvious deviations compared to the ground truth detected by the human eye. Then, residual networks (ResNet) and feature pyramidal networks (FPN) are trained on the ground truth and applied to predict the ignition time. ~Both networks are capable of detecting ignition with significantly higher accuracy and precision. Besides, influences of input data and depth of networks on the prediction performance of a trained model are examined. ~The current study shows that the hierarchical feature extraction of the convolutions networks clearly facilitates data evaluation for high-speed optical measurements and could be transferred to other solid fuel experiments with similar boundary conditions.
Representing the special linear group with block unitriangular matrices

Abstract We prove that every element of the special linear group can be represented as the product of at most six block unitriangular matrices, and that there exist matrices for which six products are necessary, independent of indexing. We present an analogous result for the general linear group. These results serve as general statements regarding the representational power of alternating linear updates. The factorizations and lower bounds of this work immediately imply tight estimates on the expressive power of linear affine coupling blocks in machine learning.
Transformer-based Sequence Labeling for Audio Classification based on MFCCs

Audio classification is vital in areas such as speech and music recognition. Feature extraction from the audio signal, such as Mel-Spectrograms and MFCCs, is a critical step in audio classification. These features are transformed into spectrograms for classification. Researchers have explored various techniques, including traditional machine and deep learning methods to classify spectrograms, but these can be computationally expensive. To simplify this process, a more straightforward approach inspired by sequence classification in NLP can be used. This paper proposes a Transformer-encoder-based model for audio classification using MFCCs. The model was benchmarked against the ESC-50, Speech Commands v0.02 and UrbanSound8k datasets and has shown strong performance, with the highest accuracy of 95.2% obtained upon training the model on the UrbanSound8k dataset. The model consisted of a mere 127,544 total parameters, making it light-weight yet highly efficient at the audio classification task.
Density functional descriptions of interfacial electronic structure

Heterogeneous interfaces are central to many energy-related applications in the nanoscale. From the first-principles electronic structure perspective, one of the outstanding problems is accurately and efficiently calculating how the frontier quasiparticle levels of one component are aligned in energy with those of another at the interface, i.e., the so-called interfacial band alignment or level alignment. The alignment or the energy offset of these frontier levels is phenomenologically associated with the charge-transfer barrier across the interface and therefore dictates the interfacial dynamics. Although many-body perturbation theory provides a formally rigorous framework for computing the interfacial quasiparticle electronic structure, it is often associated with a high computational cost and is limited by its perturbative nature. It is, therefore, of great interest to develop practical alternatives, preferably based on density functional theory (DFT), which is known for its balance between efficiency and accuracy. However, conventional developments of density functionals largely focus on total energies and thermodynamic properties, and the design of functionals aiming for interfacial electronic structure is only emerging recently. This Review is dedicated to a self-contained narrative of the interfacial electronic structure problem and the efforts of the DFT community in tackling it. Since interfaces are closely related to surfaces, we first discuss the key physics behind the surface and interface electronic structure, namely, the image potential and the gap renormalization. This is followed by a review of early examinations of the surface exchange-correlation hole and the exchange-correlation potential, which are central quantities in DFT. Finally, we survey two modern endeavors in functional development that focus on the interfacial electronic structure, namely, the dielectric-dependent hybrids and local hybrids.
Consolidator: Mergeable Adapter with Grouped Connections for Visual Adaptation

Recently, transformers have shown strong ability as visual feature extractors, surpassing traditional convolution-based models in various scenarios. However, the success of vision transformers largely owes to their capacity to accommodate numerous parameters. As a result, new challenges for adapting large models to downstream tasks arise. On the one hand, classic fine-tuning tunes all parameters in a huge model for every task and thus easily falls into overfitting, leading to inferior performance. On the other hand, on resource-limited devices, fine-tuning stores a full copy of parameters and thus is usually impracticable for the shortage of storage space. However, few works have focused on how to efficiently and effectively transfer knowledge in a vision transformer. Existing methods did not dive into the properties of visual features, leading to inferior performance. Moreover, some of them bring heavy inference cost though benefiting storage. To tackle these problems, we propose consolidator to modify the pre-trained model with the addition of a small set of tunable parameters to temporarily store the task-specific knowledge while freezing the backbone model. Motivated by the success of group-wise convolution, we adopt grouped connections across the features extracted by fully connected layers to construct tunable parts in a consolidator. To further enhance the model's capacity to transfer knowledge under a constrained storage budget and keep inference efficient, we consolidate the parameters in two stages: 1. between adaptation and storage, and 2. between loading and inference. On a series of downstream visual tasks, our consolidator can reach up to 7.56 better accuracy than full fine-tuning with merely 0.35% parameters, and outperform state-of-the-art parameter-efficient tuning methods by a clear margin. Code is available at https://github.com/beyondhtx/Consolidator.
Absolute continuity of the integrated density of states in the localized regime

We establish the absolute continuity of the integrated density of states (IDS) for quasi-periodic Schr\"odinger operators with a large trigonometric potential and Diophantine frequency. This partially solves Eliasson's open problem in 2002. Furthermore, this result can be extended to a class of quasi-periodic long-range operators on $\ell^2(\Z^d)$. Our proof is based on stratified quantitative almost reducibility results of dual cocycles. Specifically, we prove that a generic analytic one-parameter family of cocycles, sufficiently close to constant coefficients, is reducible except for a zero Hausdorff dimension set of parameters. This result affirms Eliasson's conjecture in 2017.
PathRTM: Real-time prediction of KI-67 and tumor-infiltrated lymphocytes

In this paper, we introduce PathRTM, a novel deep neural network detector based on RTMDet, for automated KI-67 proliferation and tumor-infiltrated lymphocyte estimation. KI-67 proliferation and tumor-infiltrated lymphocyte estimation play a crucial role in cancer diagnosis and treatment. PathRTM is an extension of the PathoNet work, which uses single pixel keypoints for within each cell. We demonstrate that PathRTM, with higher-level supervision in the form of bounding box labels generated automatically from the keypoints using NuClick, can significantly improve KI-67 proliferation and tumorinfiltrated lymphocyte estimation. Experiments on our custom dataset show that PathRTM achieves state-of-the-art performance in KI-67 immunopositive, immunonegative, and lymphocyte detection, with an average precision (AP) of 41.3%. Our results suggest that PathRTM is a promising approach for accurate KI-67 proliferation and tumor-infiltrated lymphocyte estimation, offering annotation efficiency, accurate predictive capabilities, and improved runtime. The method also enables estimation of cell sizes of interest, which was previously unavailable, through the bounding box predictions.
Dynamical relaxation behaviors of a critical quench

We study the universal dynamical relaxation behaviors of a quantum XY chain following a quench, paying special attention to the case that the prequenched Hamiltonian, or the postquenched Hamiltonian, or both of them are at critical points of equilibrium quantum phase transitions. In such ``critical quench", we find very interesting real-time dynamical scaling behaviors and the crossover phenomena between them. For a quench from a noncritical point to a critical point, we find that, compared to the noncritical quench, the universal power-law scaling behavior does not change; however, there may be a crossover between the exponential decaying behavior and the power-law scaling. For a quench from a critical point to a noncritical point, the power-law scaling behaviors $t^{-3/2}$ and $t^{-3/4}$ in the noncritical quenches may be changed to $t^{-1}$ and $t^{-1/2}$, respectively. If the prequenched Hamiltonian is set to be a point that is close to but not exactly at a critical point, we find interesting crossover phenomena between different power-law scaling behaviors. We also study the quench from the vicinity of a multicritical point, we find crossover behaviors that are induced by a different mechanism, and new crossover exponent is found. All the results are related to the gap-closing properties of the energy spectrum of the critical points.
RViDeformer: Efficient Raw Video Denoising Transformer with a Larger Benchmark Dataset

In recent years, raw video denoising has garnered increased attention due to the consistency with the imaging process and well-studied noise modeling in the raw domain. However, two problems still hinder the denoising performance. Firstly, there is no large dataset with realistic motions for supervised raw video denoising, as capturing noisy and clean frames for real dynamic scenes is difficult. To address this, we propose recapturing existing high-resolution videos displayed on a 4K screen with high-low ISO settings to construct noisy-clean paired frames. In this way, we construct a video denoising dataset (named as ReCRVD) with 120 groups of noisy-clean videos, whose ISO values ranging from 1600 to 25600. Secondly, while non-local temporal-spatial attention is beneficial for denoising, it often leads to heavy computation costs. We propose an efficient raw video denoising transformer network (RViDeformer) that explores both short and long-distance correlations. Specifically, we propose multi-branch spatial and temporal attention modules, which explore the patch correlations from local window, local low-resolution window, global downsampled window, and neighbor-involved window, and then they are fused together. We employ reparameterization to reduce computation costs. Our network is trained in both supervised and unsupervised manners, achieving the best performance compared with state-of-the-art methods. Additionally, the model trained with our proposed dataset (ReCRVD) outperforms the model trained with previous benchmark dataset (CRVD) when evaluated on the real-world outdoor noisy videos. Our code and dataset will be released after the acceptance of this work.
End-to-End Lane detection with One-to-Several Transformer

Although lane detection methods have shown impressive performance in real-world scenarios, most of methods require post-processing which is not robust enough. Therefore, end-to-end detectors like DEtection TRansformer(DETR) have been introduced in lane detection.However, one-to-one label assignment in DETR can degrade the training efficiency due to label semantic conflicts. Besides, positional query in DETR is unable to provide explicit positional prior, making it difficult to be optimized. In this paper, we present the One-to-Several Transformer(O2SFormer). We first propose the one-to-several label assignment, which combines one-to-many and one-to-one label assignment to solve label semantic conflicts while keeping end-to-end detection. To overcome the difficulty in optimizing one-to-one assignment. We further propose the layer-wise soft label which dynamically adjusts the positive weight of positive lane anchors in different decoder layers. Finally, we design the dynamic anchor-based positional query to explore positional prior by incorporating lane anchors into positional query. Experimental results show that O2SFormer with ResNet50 backbone achieves 77.83% F1 score on CULane dataset, outperforming existing Transformer-based and CNN-based detectors. Futhermore, O2SFormer converges 12.5x faster than DETR for the ResNet18 backbone.
Learning Self-Prior for Mesh Inpainting Using Self-Supervised Graph Convolutional Networks

This study presents a self-prior-based mesh inpainting framework that requires only an incomplete mesh as input, without the need for any training datasets. Additionally, our method maintains the polygonal mesh format throughout the inpainting process without converting the shape format to an intermediate, such as a voxel grid, a point cloud, or an implicit function, which are typically considered easier for deep neural networks to process. To achieve this goal, we introduce two graph convolutional networks (GCNs): single-resolution GCN (SGCN) and multi-resolution GCN (MGCN), both trained in a self-supervised manner. Our approach refines a watertight mesh obtained from the initial hole filling to generate a completed output mesh. Specifically, we train the GCNs to deform an oversmoothed version of the input mesh into the expected completed shape. To supervise the GCNs for accurate vertex displacements, despite the unknown correct displacements at real holes, we utilize multiple sets of meshes with several connected regions marked as fake holes. The correct displacements are known for vertices in these fake holes, enabling network training with loss functions that assess the accuracy of displacement vectors estimated by the GCNs. We demonstrate that our method outperforms traditional dataset-independent approaches and exhibits greater robustness compared to other deep-learning-based methods for shapes that less frequently appear in shape datasets.
A Dynamic Model for Frequency Response Optimization in Photovoltaic Visible Light Communication

Photovoltaic (PV) modules are recently employed in photovoltaic visible light communication (PVLC) for simultaneous energy harvesting and visible light communication. A PV-based receiver features a large signal output, easy optical alignment, and self-powered operation. However, the conventional PVLC model fails to accurately capture the factors affecting the PV module's frequency response. In this article, we systematically investigate the internal impedance dynamic of PV modules and how that affects PV frequency response under different illuminances. We propose a simplified yet accurate dynamic AC model for PV detection to capture the frequency response characteristics of a self-powered PV module. The proposed model is validated with the impedance spectroscopy characterization methodologies. Experimental results show that a PV module's internal resistance and capacitance depend on incident illuminance, affecting PV's frequency response. The bandwidth is exacerbated under indoor environments with low illuminance levels due to the increased internal resistance of PV modules. We show that adjusting the forward bias conditions can simultaneously reduce the resistance and capacitance values. With the optimization of equivalent trans-impedance based on our proposed model, the data rate of a Cadmium telluride (CdTe) PV module achieves a 3.8 times enhancement under a home scenario illuminance (200 lux). We also demonstrate that the bit error rate (BER) of a 5-Mbit/s eight-level pulse amplitude modulation (PAM8) signal can be reduced from 9.8 × 10−2 to 1.4 × 10−3 by maximizing the transimpedance gain-bandwidth product. Besides frequency response optimization, the dynamic model is also valuable when investigating other issues in PVLC, such as PV shading and PV layout optimization.
First- and Second-Order Bounds for Adversarial Linear Contextual Bandits

We consider the adversarial linear contextual bandit setting, which allows for the loss functions associated with each of $K$ arms to change over time without restriction. Assuming the $d$-dimensional contexts are drawn from a fixed known distribution, the worst-case expected regret over the course of $T$ rounds is known to scale as $\tilde O(\sqrt{Kd T})$. Under the additional assumption that the density of the contexts is log-concave, we obtain a second-order bound of order $\tilde O(K\sqrt{d V_T})$ in terms of the cumulative second moment of the learner's losses $V_T$, and a closely related first-order bound of order $\tilde O(K\sqrt{d L_T^*})$ in terms of the cumulative loss of the best policy $L_T^*$. Since $V_T$ or $L_T^*$ may be significantly smaller than $T$, these improve over the worst-case regret whenever the environment is relatively benign. Our results are obtained using a truncated version of the continuous exponential weights algorithm over the probability simplex, which we analyse by exploiting a novel connection to the linear bandit setting without contexts.
Dynamic Updating of Clinical Survival Prediction Models in a Rapidly Changing Environment

Over time, the performance of clinical prediction models may deteriorate due to changes in clinical management, data quality, disease risk and/or patient mix. Such prediction models must be updated in order to remain useful. Here, we investigate methods for discrete and dynamic model updating of clinical survival prediction models based on refitting, recalibration and Bayesian updating. In contrast to discrete or one-time updating, dynamic updating refers to a process in which a prediction model is repeatedly updated with new data. Motivated by infectious disease settings, our focus was on model performance in rapidly changing environments. We first compared the methods using a simulation study. We simulated scenarios with changing survival rates, the introduction of a new treatment and predictors of survival that are rare in the population. Next, the updating strategies were applied to patient data from the QResearch database, an electronic health records database from general practices in the UK, to study the updating of a model for predicting 70-day covid-19 related mortality. We found that a dynamic updating process outperformed one-time discrete updating in the simulations. Bayesian dynamic updating has the advantages of making use of knowledge from previous updates and requiring less data compared to refitting.