Miracle-dz/UniFAR-FTUs huggingface dataset:
Flat Covers in the Category of Quasi-coherent Sheaves Over the Projective Line

Abstract In this paper we prove the existence of a flat cover and a cotorsion envelope for any quasi-coherent sheaf over the projective line , where R is any commutative ring. We first prove a general result that guarantees the existence of ℱ-covers and ℱ⊥-envelopes in the general setting of a Grothendieck category (not necessarily with enough projectives) provided that the class ℱ satisfies some “standard” conditions. This will generalize some results of the earlier work. [Aldrich, S. T. , Enochs, E. , García Rozas, J. R. , Oyonarte, L. (2001). Covers and envelopes in Grothendieck categories. Flat covers of complexes with applications. J. Algebra 243:615–630].
GAMMA-RAY AND RADIO PROPERTIES OF SIX PULSARS DETECTED BY THE FERMI LARGE AREA TELESCOPE

We report the detection of pulsed γ-rays for PSRs J0631+1036, J0659+1414, J0742–2822, J1420–6048, J1509–5850, and J1718–3825 using the Large Area Telescope on board the Fermi Gamma-ray Space Telescope (formerly known as GLAST). Although these six pulsars are diverse in terms of their spin parameters, they share an important feature: their γ-ray light curves are (at least given the current count statistics) single peaked. For two pulsars, there are hints for a double-peaked structure in the light curves. The shapes of the observed light curves of this group of pulsars are discussed in the light of models for which the emission originates from high up in the magnetosphere. The observed phases of the γ-ray light curves are, in general, consistent with those predicted by high-altitude models, although we speculate that the γ-ray emission of PSR J0659+1414, possibly featuring the softest spectrum of all Fermi pulsars coupled with a very low efficiency, arises from relatively low down in the magnetosphere. High-quality radio polarization data are available showing that all but one have a high degree of linear polarization. This allows us to place some constraints on the viewing geometry and aids the comparison of the γ-ray light curves with high-energy beam models.
Characterization of porous GaP by photoacoustic spectroscopy: The relation between band-gap widening and visible photoluminescence

Photoacoustic and Raman spectroscopy as well as photoluminescence (PL) measurements have been used to investigate the effect of band-gap widening in porous GaP. A correlation between a blueshift of the absorption edge observed during photoacoustic measurements, accompanied by PL emissions in the blue and ultraviolet regions, and the occurance of a vibrational mode related to surface phonons is shown. Based on a simple scattering model, the structure of porous GaP is assumed to have a cylinderlike shape.
S-wave positron scattering by hydrogen atoms

The S-wave scattering of positrons by hydrogen atoms is studied using the coupled static approximation with 26 correlation functions for energies up to the excitation threshold of hydrogen. Up to a certain energy, the formulation satisfies a bound principle. Below the positronium formation threshold, the calculated phase shifts are very close to those of Bhatia et al (1971). Above this threshold, the R matrix elements and the cross sections are very different from those of other earlier calculations. No resonances were detected in a search with energy spacing of about 0.02 eV over the region just below the excitation threshold.
Waveguide discontinuity analysis with a coupled finite-boundary element method

A numerical method for the analysis of waveguide discontinuities is proposed. The approach is based on the coupling of the finite-element and boundary-element methods. The respective merits of these methods are extracted to yield a much faster solution and to enhance computation efficiency. A general procedure is described using quadratic elements approximation, and the validity and efficiency of the method is demonstrated in the cases of a three-port H-plane ferrite waveguide Y junction and of a right-angle corner bend with and without dielectric loading. Comparisons of the present results with those obtained using the finite-element approach are made and shown to be in good agreement. An experimental result is also presented. The validity of the technique can be easily verified. >
Majorana-magnon interactions in topological Shiba chains

A chain of magnetic impurities deposited on the surface of a superconductor can form a topological Shiba band that supports Majorana zero modes and holds a promise for topological quantum computing. Yet, most experiments scrutinizing these zero modes rely on transport measurements, which only capture local properties. Here we propose to leverage the intrinsic dynamics of the magnetic impurities to access their non-local character. We use linear response theory to determine the dynamics of the uniform magnonic mode in the presence of external $ac$ magnetic fields and coupling the Shiba electrons. We demonstrate that this mode, which spreads over the entire chain of atoms, becomes imprinted with the parity of the ground state and, moreover, can discriminate between Majorana and trivial zero modes located at the ends of the chain. Our approach offers a non-invasive alternative to the scanning tunneling microscopy techniques used to probe Majorana zero modes. Conversely, the magnons could facilitate the manipulation of Majorana zero modes in topological Shiba chains.
The Probabilistic Stability of Stochastic Gradient Descent

Characterizing and understanding the stability of Stochastic Gradient Descent (SGD) remains an open problem in deep learning. A common method is to utilize the convergence of statistical moments, esp. the variance, of the parameters to quantify the stability. We revisit the definition of stability for SGD and propose using the $\textit{convergence in probability}$ condition to define the $\textit{probabilistic stability}$ of SGD. The probabilistic stability sheds light on a fundamental question in deep learning theory: how SGD selects a meaningful solution for a neural network from an enormous number of possible solutions that may severely overfit. We show that only through the lens of probabilistic stability does SGD exhibit rich and practically relevant phases of learning, such as the phases of the complete loss of stability, incorrect learning where the model captures incorrect data correlation, convergence to low-rank saddles, and correct learning where the model captures the correct correlation. These phase boundaries are precisely quantified by the Lyapunov exponents of the dynamics. The obtained phase diagrams imply that SGD prefers low-rank saddles in a neural network when the underlying gradient is noisy, thereby influencing the learning performance.
New trends for the Kondo effect in nanostructures

The Kondo effect in confined nanostructures (quantum dots) provides a testbed for a variety of physical behaviours involving strong electronic correlations. Here some extensions of the Kondo effect beyond the standard single-impurity Anderson model are reviewed. Apart from their fundamental interest, these issues may also open new roads for low-temperature spintronics.
A Small-Scale Switch Transformer and NLP-based Model for Clinical Narratives Classification

In recent years, Transformer-based models such as the Switch Transformer have achieved remarkable results in natural language processing tasks. However, these models are often too complex and require extensive pre-training, which limits their effectiveness for small clinical text classification tasks with limited data. In this study, we propose a simplified Switch Transformer framework and train it from scratch on a small French clinical text classification dataset at CHU Sainte-Justine hospital. Our results demonstrate that the simplified small-scale Transformer models outperform pre-trained BERT-based models, including DistillBERT, CamemBERT, FlauBERT, and FrALBERT. Additionally, using a mixture of expert mechanisms from the Switch Transformer helps capture diverse patterns; hence, the proposed approach achieves better results than a conventional Transformer with the self-attention mechanism. Finally, our proposed framework achieves an accuracy of 87\%, precision at 87\%, and recall at 85\%, compared to the third-best pre-trained BERT-based model, FlauBERT, which achieved an accuracy of 84\%, precision at 84\%, and recall at 84\%. However, Switch Transformers have limitations, including a generalization gap and sharp minima. We compare it with a multi-layer perceptron neural network for small French clinical narratives classification and show that the latter outperforms all other models.
Dynamic Regret for Online Composite Optimization

This paper investigates online composite optimization in dynamic environments, where each objective or loss function contains a time-varying nondifferentiable regularizer. To resolve it, an online proximal gradient algorithm is studied for two distinct scenarios, including convex and strongly convex objectives without the smooth condition. In both scenarios, unlike most of works, an extended version of the conventional path variation is employed to bound the considered performance metric, i.e., dynamic regret. In the convex scenario, a bound $\mathcal{O}(\sqrt{T^{1-\beta}D_\beta(T)+T})$ is obtained which is comparable to the best-known result, where $D_\beta(T)$ is the extended path variation with $\beta\in[0,1)$ and $T$ being the total number of rounds. In strongly convex case, a bound $\mathcal{O}(\log T(1+T^{-\beta}D_\beta(T)))$ on the dynamic regret is established. In the end, numerical examples are presented to support the theoretical findings.
RLOR: A Flexible Framework of Deep Reinforcement Learning for Operation Research

Reinforcement learning has been applied in operation research and has shown promise in solving large combinatorial optimization problems. However, existing works focus on developing neural network architectures for certain problems. These works lack the flexibility to incorporate recent advances in reinforcement learning, as well as the flexibility of customizing model architectures for operation research problems. In this work, we analyze the end-to-end autoregressive models for vehicle routing problems and show that these models can benefit from the recent advances in reinforcement learning with a careful re-implementation of the model architecture. In particular, we re-implemented the Attention Model and trained it with Proximal Policy Optimization (PPO) in CleanRL, showing at least 8 times speed up in training time. We hereby introduce RLOR, a flexible framework for Deep Reinforcement Learning for Operation Research. We believe that a flexible framework is key to developing deep reinforcement learning models for operation research problems. The code of our work is publicly available at https://github.com/cpwan/RLOR.
Towards Transparent, Reusable, and Customizable Data Science in Computational Notebooks

Data science workflows are human-centered processes involving on-demand programming and analysis. While programmable and interactive interfaces such as widgets embedded within computational notebooks are suitable for these workflows, they lack robust state management capabilities and do not support user-defined customization of the interactive components. The absence of such capabilities hinders workflow reusability and transparency while limiting the scope of exploration of the end-users. In response, we developed Magneton, a framework for authoring interactive widgets within computational notebooks that enables transparent, reusable, and customizable data science workflows. The framework enhances existing widgets to support fine-grained interaction history management, reusable states, and user-defined customizations. We conducted three case studies in a real-world knowledge graph construction and serving platform to evaluate the effectiveness of these widgets. Based on the observations, we discuss future implications of employing Magneton widgets for general-purpose data science workflows.
Top-Down Visual Attention from Analysis by Synthesis

Current attention algorithms (e.g., self-attention) are stimulus-driven and highlight all the salient objects in an image. However, intelligent agents like humans often guide their attention based on the high-level task at hand, focusing only on task-related objects. This ability of task-guided top-down attention provides task-adaptive representation and helps the model generalize to various tasks. In this paper, we consider top-down attention from a classic Analysis-by-Synthesis (AbS) perspective of vision. Prior work indicates a functional equivalence between visual attention and sparse reconstruction; we show that an AbS visual system that optimizes a similar sparse reconstruction objective modulated by a goal-directed top-down signal naturally simulates top-down attention. We further propose Analysis-by-Synthesis Vision Transformer (AbSViT), which is a top-down modulated ViT model that variationally approximates AbS, and achieves controllable top-down attention. For real-world applications, AbSViT consistently improves over baselines on Vision-Language tasks such as VQA and zero-shot retrieval where language guides the top-down attention. AbSViT can also serve as a general backbone, improving performance on classification, semantic segmentation, and model robustness. Project page: https://sites.google. com/view/absvit.
Exponential quantum speedup in simulating coupled classical oscillators*

We study the problem of simulating the time evolution of a system of 2n classical coupled oscillators (e.g., 2n balls connected by springs) on a quantum computer. We map Newton’s equation for harmonic potentials to Schrödinger’s equation, such that the amplitudes of an $\mathcal{O}(n)$-qubit quantum state encode the momenta and displacements of the 2n classical oscillators. Given oracle access to the masses and spring constants, we describe a quantum algorithm with query and time complexity poly (n) that solves this problem when certain parameters are polynomially bounded and the initial state is easy to prepare. As an example application, we apply our quantum algorithm to efficiently estimate the normalized kinetic energy of an oscillator at any time. We then show that any classical algorithm solving the same problem must make $2^{\Omega(n)}$ queries to the oracle and we also show that when the oracles are instantiated by poly (n)-size circuits, the problem is BQP-complete. Thus, our approach solves a potentially practical application with an exponential speedup over classical computers.
Zero-guidance Segmentation Using Zero Segment Labels

The joint visual-language model CLIP has enabled new and exciting applications, such as open-vocabulary segmentation, which can locate any segment given an arbitrary text query. In our research, we ask whether it is possible to discover semantic segments without any user guidance in the form of text queries or predefined classes, and label them using natural language automatically? We propose a novel problem zero-guidance segmentation and the first baseline that leverages two pre-trained generalist models, DINO and CLIP, to solve this problem without any fine-tuning or segmentation dataset. The general idea is to first segment an image into small over-segments, encode them into CLIP’s visual-language space, translate them into text labels, and merge semantically similar segments together. The key challenge, however, is how to encode a visual segment into a segment-specific embedding that balances global and local context information, both useful for recognition. Our main contribution is a novel attention-masking technique that balances the two contexts by analyzing the attention layers inside CLIP. We also introduce several metrics for the evaluation of this new task. With CLIP’s innate knowledge, our method can precisely locate the Mona Lisa painting among a museum crowd (Figure 1). More results are available at https://zero-guide-seg.github. io/.
Planning-oriented Autonomous Driving

Modern autonomous driving system is characterized as modular tasks in sequential order, i.e., perception, prediction, and planning. In order to perform a wide diversity of tasks and achieve advanced-level intelligence, contemporary approaches either deploy standalone models for individual tasks, or design a multi-task paradigm with separate heads. However, they might suffer from accumulative errors or deficient task coordination. Instead, we argue that a favorable framework should be devised and optimized in pursuit of the ultimate goal, i.e., planning of the self-driving car. Oriented at this, we revisit the key components within perception and prediction, and prioritize the tasks such that all these tasks contribute to planning. We introduce Unified Autonomous Driving (UniAD), a comprehensive framework up-to-date that incorporates full-stack driving tasks in one network. It is exquisitely devised to leverage advantages of each module, and provide complementary feature abstractions for agent interaction from a global perspective. Tasks are communicated with unified query interfaces to facilitate each other toward planning. We instantiate UniAD on the challenging nuScenes benchmark. With extensive ablations, the effectiveness of using such a philosophy is proven by substantially outperforming previous state-of-the-arts in all aspects. Code and models are public.
Optimizing the mode-to-pump ratio in end-pumped quasi-three-level Nd-doped lasers considering the energy-transfer upconversion

The reabsorption losses considering energy-transfer-upconversion effects and the losses due to energy-transfer upconversion are derived as a function of the mode-to-pump ratio and the output coupler transmission in end-pumped quasi-three-level Nd-doped lasers, respectively. The dependence of thermally induced diffraction losses on the mode-to-pump ratio and the output coupler transmission is investigated with the energy-transfer-upconversion effects considered. The mode-to-pump ratio with and without thermally induced diffraction losses considering the energy-transfer upconversion is optimized. The results show that the optimum mode-to-pump ratio with the upconversion effects is less than that in which the energy-transfer-upconversion effects are neglected.
Development and validation of a natural language processing algorithm to pseudonymize documents in the context of a clinical data warehouse

OBJECTIVE The objective of this study is to address the critical issue of deidentification of clinical reports to allow access to data for research purposes, while ensuring patient privacy. The study highlights the difficulties faced in sharing tools and resources in this domain and presents the experience of the Greater Paris University Hospitals (AP-HP for Assistance Publique-Hôpitaux de Paris) in implementing a systematic pseudonymization of text documents from its Clinical Data Warehouse. METHODS We annotated a corpus of clinical documents according to 12 types of identifying entities and built a hybrid system, merging the results of a deep learning model as well as manual rules. RESULTS AND DISCUSSION Our results show an overall performance of 0.99 of F1-score. We discuss implementation choices and present experiments to better understand the effort involved in such a task, including dataset size, document types, language models, or rule addition. We share guidelines and code under a 3-Clause BSD license.
Is ChatGPT A Good Keyphrase Generator? A Preliminary Study

The emergence of ChatGPT has recently garnered significant attention from the computational linguistics community. To demonstrate its capabilities as a keyphrase generator, we conduct a preliminary evaluation of ChatGPT for the keyphrase generation task. We evaluate its performance in various aspects, including keyphrase generation prompts, keyphrase generation diversity, and long document understanding. Our evaluation is based on six benchmark datasets, and we adopt the prompt suggested by OpenAI while extending it to six candidate prompts. We find that ChatGPT performs exceptionally well on all six candidate prompts, with minor performance differences observed across the datasets. Based on our findings, we conclude that ChatGPT has great potential for keyphrase generation. Moreover, we discover that ChatGPT still faces challenges when it comes to generating absent keyphrases. Meanwhile, in the final section, we also present some limitations and future expansions of this report.
Visually-Prompted Language Model for Fine-Grained Scene Graph Generation in an Open World

Scene Graph Generation (SGG) aims to extract relationships in images for vision understanding. Although recent works have made steady progress on SGG, they still suffer long-tail distribution issues that tail-predicates are more costly to train and hard to distinguish due to a small amount of annotated data compared to frequent predicates. Existing re-balancing strategies try to handle it via prior rules but are still confined to pre-defined conditions, which are not scalable for various models and datasets. In this paper, we propose a Cross-modal prediCate boosting (CaCao) framework, where a visually-prompted language model is learned to generate diverse fine-grained predicates in a low-resource way. The proposed CaCao can be applied in a plug-and-play fashion and automatically strengthen existing SGG to tackle the long-tailed problem. Based on that, we further introduce a novel Entangled cross-modal prompt approach for open-world predicate scene graph generation (Epic), where models can generalize to unseen predicates in a zero-shot manner. Comprehensive experiments on three benchmark datasets show that CaCao consistently boosts the performance of multiple scene graph generation models in a model-agnostic way. Moreover, our Epic achieves competitive performance on open-world predicate prediction. The data and code for this paper are publicly available.1
A comparison of graph neural networks for malware classification

Managing the threat posed by malware requires accurate detection and classification techniques. Traditional detection strategies, such as signature scanning, rely on manual analysis of malware to extract relevant features, which is labor intensive and requires expert knowledge. Function call graphs consist of a set of program functions and their inter-procedural calls, providing a rich source of information that can be leveraged to classify malware without the labor intensive feature extraction step of traditional techniques. In this research, we treat malware classification as a graph classification problem. Based on Local Degree Profile features, we train a wide range of Graph Neural Network (GNN) architectures to generate embeddings which we then classify. We find that our best GNN models outperform previous comparable research involving the well-known MalNet-Tiny Android malware dataset. In addition, our GNN models do not suffer from the overfitting issues that commonly afflict non-GNN techniques, although GNN models require longer training times.