Miracle-dz/UniFAR-FTUs huggingface dataset:
Exceptional algebraic curves for infinite groups of $\textrm{PGL}(n,\mathbb{C})$

We characterize algebraic curves in $\mathbb{P}^{n-1}_\mathbb{C}$ invariant under an infinite subgroup of $\textrm{PGL}(n,\mathbb{C})$, $n\geq 3$. More precisely, it is shown that given a one-dimensional algebraic set in $\mathbb{P}^{n-1}_\mathbb{C}$ which is invariant under an infinite subgroup of projective transformations should satisfy that each irreducible non-degenerate component is projectively equivalent to a monomial curve.
Semantics of multimodal adjoint type theory

We show that contrary to appearances, Multimodal Type Theory (MTT) over a 2-category M can be interpreted in any M-shaped diagram of categories having, and functors preserving, M-sized limits, without the need for extra left adjoints. This is achieved by a construction called "co-dextrification" that co-freely adds left adjoints to any such diagram, which can then be used to interpret the "context lock" functors of MTT. Furthermore, if any of the functors in the diagram have right adjoints, these can also be internalized in type theory as negative modalities in the style of FitchTT. We introduce the name Multimodal Adjoint Type Theory (MATT) for the resulting combined general modal type theory. In particular, we can interpret MATT in any finite diagram of toposes and geometric morphisms, with positive modalities for inverse image functors and negative modalities for direct image functors.
Time Associated Meta Learning for Clinical Prediction

Rich Electronic Health Records (EHR), have created opportunities to improve clinical processes using machine learning methods. Prediction of the same patient events at different time horizons can have very different applications and interpretations; however, limited number of events in each potential time window hurts the effectiveness of conventional machine learning algorithms. We propose a novel time associated meta learning (TAML) method to make effective predictions at multiple future time points. We view time-associated disease prediction as classification tasks at multiple time points. Such closely-related classification tasks are an excellent candidate for model-based meta learning. To address the sparsity problem after task splitting, TAML employs a temporal information sharing strategy to augment the number of positive samples and include the prediction of related phenotypes or events in the meta-training phase. We demonstrate the effectiveness of TAML on multiple clinical datasets, where it consistently outperforms a range of strong baselines. We also develop a MetaEHR package for implementing both time-associated and time-independent few-shot prediction on EHR data.
Transport in helical fluid turbulence

Kinetic helicity (hereafter helicity) is defined by the correlation between the velocity and the flow-aligned vorticity. Helicity, as well as energy, is an inviscid invariant of the hydrodynamic equations. In contrast to energy, a measure of the turbulent intensity, turbulent helicity, representing right- and left-handed twist associated with a fluctuating motion, provides a measure of the structural or topological property of the fluctuation. The helicity effect on the turbulent transport can be analytically obtained in the framework of the multiple-scale renormalized perturbation expansion theory through the inclusion of the non-reflectionally-symmetric part for the lowest-order (homogeneous and isotropic) velocity correlation. The physical significance of the helicity-related contribution to the momentum transport is explained. By utilizing the analytical expression of the Reynolds stress, a turbulence model with helicity effect incorporated (helicity model) is constructed. This helicity model is applied to a swirling flow to show its validity in describing the prominent properties of the flow. In addition to the transport suppression, inhomogeneous helicity coupled with a rotation can induce a large-scale flow. The results of direct numerical simulations (DNSs) confirming the global flow generation by helicity will be also reviewed, followed by several possible applications in geo- and astro-physical flow phenomena.
Variational Quantum Eigensolver for Classification in Credit Sales Risk

In this work, a classification task is realized by the Variational Quantum Eigensolver (VQE) approach. A decision situation concerns issuing commodities in the process of credit sales -- at some point a client's history may be analyzed by debt department's employee to check if the goods issuing should not be stopped due to the risk of fraud (bivalent classification). As in the classical machine learning, the data should be normalized. We describe the two-step process of data normalization to write each observation as a quantum state. The VQE approach allows training the parameters of a quantum circuit (so-called ansatz) to output pattern-states for each class. Observations are categorized to classes with the use of the SWAP-test. The obtained solution is compact and requires only logarithmically increasing number of qubits (due to the exponential capacity of quantum registers) -- we also present alternative classical solutions which, in fact, are quite complex. All calculations, plots, and comparisons were implemented and conduced in Python language environment. Source codes of the simulations for individual examples of quantum classification can be found in the source code repository. The version of a simulator directly used in the article is in the Zenodo version of the source code.
An efficient algorithm for integer lattice reduction

A lattice of integers is the collection of all linear combinations of a set of vectors for which all entries of the vectors are integers and all coefficients in the linear combinations are also integers. Lattice reduction refers to the problem of finding a set of vectors in a given lattice such that the collection of all integer linear combinations of this subset is still the entire original lattice and so that the Euclidean norms of the subset are reduced. The present paper proposes simple, efficient iterations for lattice reduction which are guaranteed to reduce the Euclidean norms of the basis vectors (the vectors in the subset) monotonically during every iteration. Each iteration selects the basis vector for which projecting off (with integer coefficients) the components of the other basis vectors along the selected vector minimizes the Euclidean norms of the reduced basis vectors. Each iteration projects off the components along the selected basis vector and efficiently updates all information required for the next iteration to select its best basis vector and perform the associated projections.
Locally universal C*-algebras with computable presentations

The Kirchberg Embedding Problem (KEP) asks if every C*-algebra embeds into an ultrapower of the Cuntz algebra $\mathcal{O}_2$. In an effort to provide a negative solution to the KEP and motivated by the recent refutation of the Connes Embedding Problem, we establish two computability-theoretic consequences of a positive solution to KEP. Both of our results follow from the a priori weaker assumption that there exists a locally universal C*-algebra with a computable presentation.
Integration of Feature Selection Techniques using a Sleep Quality Dataset for Comparing Regression Algorithms

This research aims to examine the usefulness of integrating various feature selection methods with regression algorithms for sleep quality prediction. A publicly accessible sleep quality dataset is used to analyze the effect of different feature selection techniques on the performance of four regression algorithms - Linear regression, Ridge regression, Lasso Regression and Random Forest Regressor. The results are compared to determine the optimal combination of feature selection techniques and regression algorithms. The conclusion of the study enriches the current literature on using machine learning for sleep quality prediction and has practical significance for personalizing sleep recommendations for individuals.
Query Learning Algorithm for Ordered Multi-Terminal Binary Decision Diagrams

We propose a query learning algorithm for ordered multi-terminal binary decision diagrams (OMTBDDs) using at most n equivalence and 2n(l\lcei\log_2 m\rceil+ 3n) membership queries by extending the algorithm for ordered binary decision diagrams (OBDDs). Tightness of our upper bounds is checked in our experiments using synthetically generated target OMTBDDs. Possibility of applying our algorithm to classification problems is also indicated in our other experiments using datasets of UCI Machine Learning Repository.
On the solid angle of a convex set

Here we analyze three dimensional analogues of the classical Crofton's formula for planar compact convex sets. In this formula a fundamental role is played by the visual angle of the convex set from an exterior point. A generalization of the visual angle to convex sets in euclidian space is the visual solid angle. This solid angle, being an spherically convex set in the unit sphere, has length, area and other geometric quantities to be considered. The main goal of this note is to express invariant quantities of the original convex set depending on volume, surface area and mean curvature integral by means of integrals of functions related to the solid angle.
Visualizing near-coexistence of massless Dirac electrons and ultra-massive saddle point electrons

Strong singularities in the electronic density of states amplify correlation effects and play a key role in determining the ordering instabilities in various materials. Recently high order van Hove singularities (VHSs) with diverging power-law scaling have been classified in single-band electron models. We show that the 110 surface of Bismuth exhibits high order VHS with an usually high density of states divergence \sim (E)^{-0.7}∼(E)−0.7. Detailed mapping of the surface band structure using scanning tunneling microscopy and spectroscopy combined with first-principles calculations show that this singularity occurs in close proximity to Dirac bands located at the center of the surface Brillouin zone. The enhanced power-law divergence is shown to originate from the anisotropic flattening of the Dirac band just above the Dirac node. Such near-coexistence of massless Dirac electrons and ultra-massive saddle points enables to study the interplay of high order VHS and Dirac fermions.
Causal Inference using Multivariate Generalized Linear Mixed-Effects Models with Longitudinal Data

Dynamic prediction of causal effects under different treatment regimes conditional on an individual's characteristics and longitudinal history is an essential problem in precision medicine. This is challenging in practice because outcomes and treatment assignment mechanisms are unknown in observational studies, an individual's treatment efficacy is a counterfactual, and the existence of selection bias is often unavoidable. We propose a Bayesian framework for identifying subgroup counterfactual benefits of dynamic treatment regimes by adapting Bayesian g-computation algorithm (J. Robins, 1986; Zhou, Elliott,&Little, 2019) to incorporate multivariate generalized linear mixed-effects models. Unmeasured time-invariant factors are identified as subject-specific random effects in the assumed joint distribution of outcomes, time-varying confounders, and treatment assignments. Existing methods mostly assume no unmeasured confounding and focus on balancing the observed confounder distributions between different treatments, while our method allows the presence of time-invariant unmeasured confounding. We propose a sequential ignorability assumption based on treatment assignment heterogeneity, which is analogous to balancing the latent tendency toward each treatment due to unmeasured time-invariant factors beyond the observables. We use simulation studies to assess the sensitivity of the proposed method's performance to various model assumptions. The method is applied to observational clinical data to investigate the efficacy of continuously using mycophenolate in different subgroups of scleroderma patients who were treated with the drug.
Model Sketching: Centering Concepts in Early-Stage Machine Learning Model Design

Machine learning practitioners often end up tunneling on low-level technical details like model architectures and performance metrics. Could early model development instead focus on high-level questions of which factors a model ought to pay attention to? Inspired by the practice of sketching in design, which distills ideas to their minimal representation, we introduce model sketching: a technical framework for iteratively and rapidly authoring functional approximations of a machine learning model’s decision-making logic. Model sketching refocuses practitioner attention on composing high-level, human-understandable concepts that the model is expected to reason over (e.g., profanity, racism, or sarcasm in a content moderation task) using zero-shot concept instantiation. In an evaluation with 17 ML practitioners, model sketching reframed thinking from implementation to higher-level exploration, prompted iteration on a broader range of model designs, and helped identify gaps in the problem formulation—all in a fraction of the time ordinarily required to build a model.
Properties of isolated single crystalline and textured polycrystalline nano/sub-micrometre Nd2Fe14B particles obtained from milling of HDDR powder

Textured, polycrystalline Nd2Fe14B powders, produced by dynamic hydrogenation disproportionation desorption and recombination (d-HDDR) were further processed by wet and surfactant-assisted ball milling. After 4 h of milling at 400 rpm in absolute ethanol and heptane + oleic acid, the polycrystalline d-HDDR particles had disintegrated, via intergranular fracture, into the individual grains i.e. isolated single crystalline particles of size 200 to 500 nm. An excellent degree of alignment was produced in the single crystalline particles using an applied magnetic field. This was reflected in the remanence of the field-aligned single crystalline powder (148.1 emu g−1) which was far higher than that of field-aligned un-milled d-HDDR powder (119.5 emu g−1). Milling the single crystalline powder further at 800 rpm in the same media produced polycrystalline flakes of size 0.2 to 1.0 µm. The polycrystalline flakes showed (0 0 l) in-plane texture and thus oriented edge to edge in an applied field.
Dual-Path Cross-Modal Attention for Better Audio-Visual Speech Extraction

Audiovisual target speaker extraction is the task of separating, from an audio mixture, the speaker whose face is visible in an accompanying video. Published approaches typically upsample the video or downsample the audio, then fuse the two streams using concatenation, multiplication, or cross-modal attention. This paper proposes, instead, to use a dual-path attention architecture in which the audio chunk length is comparable to the duration of a video frame. Audio is transformed by intra-chunk attention, concatenated to video features, then transformed by inter-chunk attention. Because of residual connections, the audio and video features remain logically distinct across multiple network layers, therefore dual-path audiovisual feature fusion can be performed repeatedly across multiple layers. When given 2-5-speaker mixtures constructed from the challenging LRS3 test set, results are about 7dB better than Con-vTasNet or AV-ConvTasNet, with the performance gap widening slightly as the number of speakers increases.
The role of nanoclusters in reducing hole trapping in ion implanted oxides

At the 2000 IEEE Nuclear and Space Radiation Effects Conference, it was shown that the negative shift in flatband voltage that results from hole injection is reduced in oxides that have been implanted with large doses of Al, Si, or P ions. In the present paper, we study the basic mechanism responsible for this reduced shift in the flatband voltage in more detail by comparing electron and hole trapping in Si and Ar implanted oxides. We find that in Si implanted oxides, the reduction in the shift of the flatband voltage is accompanied by the formation of entities in the oxide that have a large electron capture cross section, and that can become positively charged by photoemitting electrons. Photoluminescence studies indicate that these entities are Si nanoclusters. Oxides implanted with large doses of Ar do not form clusters, and these oxides show neither a reduction in the shift of the flatband voltage nor the formation of large capture cross-section electron traps. We show evidence that the nanoclusters reduce the shift of the flatband voltage by trapping protons formed during hole injection.
FluidLab: A Differentiable Environment for Benchmarking Complex Fluid Manipulation

Humans manipulate various kinds of fluids in their everyday life: creating latte art, scooping floating objects from water, rolling an ice cream cone, etc. Using robots to augment or replace human labors in these daily settings remain as a challenging task due to the multifaceted complexities of fluids. Previous research in robotic fluid manipulation mostly consider fluids governed by an ideal, Newtonian model in simple task settings (e.g., pouring). However, the vast majority of real-world fluid systems manifest their complexities in terms of the fluid's complex material behaviors and multi-component interactions, both of which were well beyond the scope of the current literature. To evaluate robot learning algorithms on understanding and interacting with such complex fluid systems, a comprehensive virtual platform with versatile simulation capabilities and well-established tasks is needed. In this work, we introduce FluidLab, a simulation environment with a diverse set of manipulation tasks involving complex fluid dynamics. These tasks address interactions between solid and fluid as well as among multiple fluids. At the heart of our platform is a fully differentiable physics simulator, FluidEngine, providing GPU-accelerated simulations and gradient calculations for various material types and their couplings. We identify several challenges for fluid manipulation learning by evaluating a set of reinforcement learning and trajectory optimization methods on our platform. To address these challenges, we propose several domain-specific optimization schemes coupled with differentiable physics, which are empirically shown to be effective in tackling optimization problems featured by fluid system's non-convex and non-smooth properties. Furthermore, we demonstrate reasonable sim-to-real transfer by deploying optimized trajectories in real-world settings.
Boundary-Semantic Collaborative Guidance Network With Dual-Stream Feedback Mechanism for Salient Object Detection in Optical Remote Sensing Imagery

With the increasing application of deep learning (DL) in various domains, salient object detection in optical remote sensing images (ORSIs-SOD) has attracted significant attention. However, most existing ORSI-SOD methods predominantly rely on local information from low-level features to infer salient boundary cues and supervise them using boundary ground truth (GT) but fail to sufficiently optimize and protect the local information, and almost all approaches ignore the potential advantages offered by the last layer of the decoder to maintain the integrity of saliency maps. To address these issues, we propose a novel method named boundary-semantic collaborative guidance network (BSCGNet) with dual-stream feedback mechanism. First, we propose a boundary protection calibration (BPC) module, which effectively reduces the loss of edge position information during forward propagation and suppresses noise in low-level features without relying on boundary GT. Second, based on the BPC module, a dual feature feedback complementary (DFFC) module is proposed, which aggregates boundary-semantic dual features and provides effective feedback to coordinate features across different layers, thereby enhancing cross-scale knowledge communication. Finally, to obtain more complete saliency maps, we consider the uniqueness of the last layer of the decoder for the first time and propose the adaptive feedback refinement (AFR) module, which further refines feature representation and eliminates differences between features through a unique feedback mechanism. Extensive experiments on three benchmark datasets demonstrate that BSCGNet exhibits distinct advantages in challenging scenarios and outperforms the 17 state-of-the-art (SOTA) approaches proposed in recent years. Codes and results have been released on GitHub: https://github.com/YUHsss/BSCGNet.
Polarization dependent focusing lens by use of quantized Pancharatnam–Berry phase diffractive optics

Quantized Pancharatnam–Berry phase diffractive optics using computer-generated space-variant subwavelength dielectric grating is presented. The formation of the geometrical phase is done by discrete orientation of the local subwavelength grating. We discuss a theoretical analysis and experimentally demonstrate a quantized geometrical blazed phase of polarization diffraction grating, as well as polarization dependent focusing lens for infrared radiation at wavelength 10.6 μm.
Multi label classification of Artificial Intelligence related patents using Modified D2SBERT and Sentence Attention mechanism

Patent classification is an essential task in patent information management and patent knowledge mining. It is very important to classify patents related to artificial intelligence, which is the biggest topic these days. However, artificial intelligence-related patents are very difficult to classify because it is a mixture of complex technologies and legal terms. Moreover, due to the unsatisfactory performance of current algorithms, it is still mostly done manually, wasting a lot of time and money. Therefore, we present a method for classifying artificial intelligence-related patents published by the USPTO using natural language processing technique and deep learning methodology. We use deformed BERT and sentence attention overcome the limitations of BERT. Our experiment result is highest performance compared to other deep learning methods.
A Hop Away from Everywhere: A View of the Intercontinental Long-haul Infrastructure

We present a longitudinal study of intercontinental long-haul links (LHLs) - links with latencies significantly higher than that of all other links in a traceroute path. Our study is motivated by the recognition of these LHLs as a network-layer manifestation of critical transoceanic undersea cables. We present a methodology and associated processing system for identifying long-haul links in traceroute measurements. We apply this system to a large corpus of traceroute data and report on multiple aspects of long haul connectivity including country-level prevalence, routers as international gateways, preferred long-haul destinations, and the evolution of these characteristics over a 7 year period. We identify 85,620 layer-3 links (out of 2.7M links in a large traceroute dataset) that satisfy our definition for intercontinental long haul with many of them terminating in a relatively small number of nodes. An analysis of connected components shows a clearly dominant component with a relative size that remains stable despite a significant growth of the long-haul infrastructure.
Thompson Sampling for Linear Bandit Problems with Normal-Gamma Priors

We consider Thompson sampling for linear bandit problems with finitely many independent arms, where rewards are sampled from normal distributions that are linearly dependent on unknown parameter vectors and with unknown variance. Specifically, with a Bayesian formulation we consider multivariate normal-gamma priors to represent environment uncertainty for all involved parameters. We show that our chosen sampling prior is a conjugate prior to the reward model and derive a Bayesian regret bound for Thompson sampling under the condition that the 5/2-moment of the variance distribution exist.