Miracle-dz/UniFAR-FTUs huggingface dataset:
LMSFC: A Novel Multidimensional Index based on Learned Monotonic Space Filling Curves

The recently proposed learned indexes have attracted much attention as they can adapt to the actual data and query distributions to attain better search efficiency. Based on this technique, several existing works build up indexes for multi-dimensional data and achieve improved query performance. A common paradigm of these works is to (i) map multi-dimensional data points to a one-dimensional space using a fixed space-filling curve (SFC) or its variant and (ii) then apply the learned indexing techniques. We notice that the first step typically uses a fixed SFC method, such as row-major order and z -order. It definitely limits the potential of learned multi-dimensional indexes to adapt variable data distributions via different query workloads. In this paper, we propose a novel idea of learning a space-filling curve that is carefully designed and actively optimized for efficient query processing. We also identify innovative offline and online optimization opportunities common to SFC-based learned indexes and offer optimal and/or heuristic solutions. Experimental results demonstrate that our proposed method, LMSFC, outperforms state-of-the-art non-learned or learned methods across three commonly used real-world datasets and diverse experimental settings.
Weakly-Supervised Temporal Action Localization with Bidirectional Semantic Consistency Constraint

Weakly supervised temporal action localization (WTAL) aims to classify and localize temporal boundaries of actions for the video, given only video-level category labels in the training datasets. Due to the lack of boundary information during training, existing approaches formulate WTAL as a classification problem, i.e., generating the temporal class activation map (T-CAM) for localization. However, with only classification loss, the model would be suboptimized, i.e., the action-related scenes are enough to distinguish different class labels. Regarding other actions in the action-related scene (i.e., the scene same as positive actions) as co-scene actions, this suboptimized model would misclassify the co-scene actions as positive actions. To address this misclassification, we propose a simple yet efficient method, named bidirectional semantic consistency constraint (Bi-SCC), to discriminate the positive actions from co-scene actions. The proposed Bi-SCC first adopts a temporal context augmentation to generate an augmented video that breaks the correlation between positive actions and their co-scene actions in the inter-video. Then, a semantic consistency constraint (SCC) is used to enforce the predictions of the original video and augmented video to be consistent, hence suppressing the co-scene actions. However, we find that this augmented video would destroy the original temporal context. Simply applying the consistency constraint would affect the completeness of localized positive actions. Hence, we boost the SCC in a bidirectional way to suppress co-scene actions while ensuring the integrity of positive actions, by cross-supervising the original and augmented videos. Finally, our proposed Bi-SCC can be applied to current WTAL approaches and improve their performance. Experimental results show that our approach outperforms the state-of-the-art methods on THUMOS14 and ActivityNet. The code is available at https://github.com/lgzlIlIlI/BiSCC.
Model-Free Learning and Optimal Policy Design in Multi-Agent MDPs Under Probabilistic Agent Dropout

This work studies a multi-agent Markov decision process (MDP) that can undergo agent dropout and the computation of policies for the post-dropout system based on control and sampling of the pre-dropout system. The controller's objective is to find an optimal policy that maximizes the value of the expected system given a priori knowledge of the agents' dropout probabilities. Finding an optimal policy for any specific dropout realization is a special case of this problem. For MDPs with a certain transition independence and reward separability structure, we assume that removing agents from the system forms a new MDP comprised of the remaining agents with new state and action spaces, transition dynamics that marginalize the removed agents, and rewards that are independent of the removed agents. We first show that under these assumptions, the value of the expected post-dropout system can be represented by a single MDP; this"robust MDP"eliminates the need to evaluate all $2^N$ realizations of the system, where $N$ denotes the number of agents. More significantly, in a model-free context, it is shown that the robust MDP value can be estimated with samples generated by the pre-dropout system, meaning that robust policies can be found before dropout occurs. This fact is used to propose a policy importance sampling (IS) routine that performs policy evaluation for dropout scenarios while controlling the existing system with good pre-dropout policies. The policy IS routine produces value estimates for both the robust MDP and specific post-dropout system realizations and is justified with exponential confidence bounds. Finally, the utility of this approach is verified in simulation, showing how structural properties of agent dropout can help a controller find good post-dropout policies before dropout occurs.
Imperfectly coordinated water molecules pave the way for homogeneous ice nucleation

Water freezing is ubiquitous on Earth, affecting many areas from biology to climate science and aviation technology. Probing the atomic structure in the homogeneous ice nucleation process from scratch is of great value but still experimentally unachievable. Theoretical simulations have found that ice originates from the low-mobile region with increasing abundance and persistence of tetrahedrally coordinated water molecules. However, a detailed microscopic picture of how the disordered hydrogen-bond network rearranges itself into an ordered network is still unclear. In this work, we use a deep neural network (DNN) model to"learn"the interatomic potential energy from quantum mechanical data, thereby allowing for large-scale and long molecular dynamics (MD) simulations with ab initio accuracy. The nucleation mechanism and dynamics at atomic resolution, represented by a total of 36 $\mu$s-long MD trajectories, are deeply affected by the structural and dynamical heterogeneity in supercooled water. We find that imperfectly coordinated (IC) water molecules with high mobility pave the way for hydrogen-bond network rearrangement, leading to the growth or shrinkage of the ice nucleus. The hydrogen-bond network formed by perfectly coordinated (PC) molecules stabilizes the nucleus, thus preventing it from vanishing and growing. Consequently, ice is born through competition and cooperation between IC and PC molecules. We anticipate that our picture of the microscopic mechanism of ice nucleation will provide new insights into many properties of water and other relevant materials.
Limiting eigenvalue distribution of heavy-tailed Toeplitz matrices

We consider an $N \times N$ random symmetric Toeplitz matrix with an i.i. d. input sequence drawn from a distribution that lies in the domain of attraction of an $\alpha$-stable law for $0<\alpha<2$. We show that under an appropriate scaling, its empirical eigenvalue distribution, as $N \to \infty$, converges weakly to a random symmetric probability distribution on $\mathbb{R}$, which can be described as the expected spectral measure of a certain random unbounded self-adjoint operator on $\ell^2(\mathbb{Z})$. The limiting distribution turns out to be almost surely subgaussian. Furthermore, the support of the limiting distribution is bounded almost surely if $0<\alpha<1$ and is unbounded almost surely if $1\leq \alpha<2$.
Opinion Control under Adversarial Network Perturbation: A Stackelberg Game Approach

The emerging social network platforms enable users to share their own opinions, as well as to exchange opinions with others. However, adversarial network perturbation, where malicious users intentionally spread their extreme opinions, rumors, and misinformation to others, is ubiquitous in social networks. Such adversarial network perturbation greatly influences the opinion formation of the public and threatens our societies. Thus, it is critical to study and control the influence of adversarial network perturbation. Although tremendous efforts have been made in both academia and industry to guide and control the public opinion dynamics, most of these works assume that the network is static, and ignore such adversarial network perturbation. In this work, based on the well-accepted Friedkin-Johnsen opinion dynamics model, we model the adversarial network perturbation and analyze its impact on the networks' opinion. Then, from the adversary's perspective, we analyze its optimal network perturbation, which maximally changes the network's opinion. Next, from the network defender's perspective, we formulate a Stackelberg game and aim to control the network's opinion even under such adversarial network perturbation. We devise a projected subgradient algorithm to solve the formulated Stackelberg game. Extensive simulations on real social networks validate our analysis of the adversarial network perturbation's influence and the effectiveness of the proposed opinion control algorithm.
PoseVocab: Learning Joint-structured Pose Embeddings for Human Avatar Modeling

Creating pose-driven human avatars is about modeling the mapping from the low-frequency driving pose to high-frequency dynamic human appearances, so an effective pose encoding method that can encode high-fidelity human details is essential to human avatar modeling. To this end, we present PoseVocab, a novel pose encoding method that encourages the network to discover the optimal pose embeddings for learning the dynamic human appearance. Given multi-view RGB videos of a character, PoseVocab constructs key poses and latent embeddings based on the training poses. To achieve pose generalization and temporal consistency, we sample key rotations in so(3) of each joint rather than the global pose vectors, and assign a pose embedding to each sampled key rotation. These joint-structured pose embeddings not only encode the dynamic appearances under different key poses, but also factorize the global pose embedding into joint-structured ones to better learn the appearance variation related to the motion of each joint. To improve the representation ability of the pose embedding while maintaining memory efficiency, we introduce feature lines, a compact yet effective 3D representation, to model more fine-grained details of human appearances. Furthermore, given a query pose and a spatial position, a hierarchical query strategy is introduced to interpolate pose embeddings and acquire the conditional pose feature for dynamic human synthesis. Overall, PoseVocab effectively encodes the dynamic details of human appearance and enables realistic and generalized animation under novel poses. Experiments show that our method outperforms other state-of-the-art baselines both qualitatively and quantitatively in terms of synthesis quality. Code is available at https://github.com/lizhe00/PoseVocab.
Ranked set sampling: As a cost-effective and more efficient data collection method

Ranked set sampling (RSS) is a cost-effective sampling technique that induces stratification on the population through rank orders of samples. This, in turn, provides a more structured sample than a simple random sample does with the same sample size. This yields more efficient estimators of some parameters of interest. In this method a fairly large number of randomly identified sampling units are portioned into small subsets of the same size. The units of each subset are ranked separately with respect to the characteristic of interest without using their actual measurements. The measurements of the units with some specified ranks constitute a ranked set sample. In this paper we wish to discuss theory, methods and some recently reported applications of RSS to highlight its advantages. This method could be of some particular interest to those who look for a cost-effective and more efficient data collection technique for sampling and monitoring situations.
Forming a ranking from tied evaluations: a case of an online, interactive student peer assessment system

In higher education courses, peer assessment activities are common for keeping students engaged during presentations. Defining precisely how students assess the work of others requires careful consideration. Asking the student for numeric grades is the most common method. However, students tend to assign high grades to most projects. Aggregating peer assessments, therefore, results in all projects receiving the same grade. Moreover, students might strategically assign low grades to the projects of others so that their projects will shine. Asking students to order all projects from best to worst imposes a high cognitive load on them, as studies have shown that people find it difficult to order more than a handful of items. To address these issues, we propose a novel peer rating model consisting of (a) an algorithm that elicits student assessments and (b) a protocol for aggregating grades to produce a single order. The algorithm asks students to evaluate projects and answer pairwise comparison queries. These are then aggregated into a ranking over the projects. An application based on our model was deployed and tested in a university course and showed promising results, including fewer ties between alternatives and a significant reduction in the communication load on students. These results indicate that the model provides a simple, accurate, and efficient approach to peer review.
Fractal nodal band structures

Non-Hermitian systems exhibit interesting band structures, where novel topological phenomena arise from the existence of exceptional points at which eigenvalues and eigenvectors coalesce. One important open question is how this would manifest at non-integer dimension. Here, we report on the appearance of fractal eigenvalue degeneracies and Fermi surfaces in Hermitian and non-Hermitian topological band structures. This might have profound implications on the physics of black holes and Fermi surface instability driven phenomena, such as superconductivity and charge density waves.
Intervenciones de autocuidado diádico en la insuficiencia cardíaca crónica en el contexto hospitalario: una revisión sistemática

Resumen El autocuidado diádico en la insuficiencia cardiaca crónica (ICC) es clave para garantizar la continuidad del tratamiento, disminuir las complicaciones y los reingresos, y minimizar la sobrecarga del cuidador, pero demanda estrategias específicas. El objetivo fue identificar las intervenciones de autocuidado diádico en la ICC en el contexto hospitalario mediante una revisión sistemática de la literatura en PubMed, CINAHL y PsycInfo; la calidad metodológica se valoró según las herramientas de CASPe y del Joanna Briggs Institute. Se identificaron los principales componentes de las intervenciones: formato de administración; dimensiones y estrategias utilizadas (cognitivo-actitudinal, afectiva-emocional y conductual); proveedores y receptores; instrumentos de medida utilizados; y efectividad. La mayoría de estudios mejoraron los resultados, especialmente síntomas de depresión y/o ansiedad, adherencia al tratamiento, dieta y control del peso. Se recomiendan intervenciones innovadoras que incluyan componentes de las tres dimensiones identificadas y el uso de escalas válidas, fiables y específicas para medir los resultados.
Spectrally non-overlapping background noise disturbs echolocation via acoustic masking in the CF-FM bat, Hipposideros pratti

Hipposideros pratti called at higher intensities while keeping the constant frequencies of their echolocation pulses consistent under spectrally non-overlapping background noise conditions. The noise could decrease auditory sensitivity and sharp intensity tuning, suggesting an acoustic masking effect. These results provide further evidence of negative consequences of anthropogenic noise.
On the existence of periodic orbits and basins of attraction for a piecewise- smooth system

In this article we study the existence of fixed points and 2-cycles for a dynamical system associated to a piecewise quadratic family with three parameters of bifurcation. We obtain the fixed points of the system and include an analytical study of their stability, via derivative's criteria. A computing of the basins of attraction is also presented. When varying one of the parameters in a real interval, one of the fixed point has a basin of attraction consisting of a semi-bounded interval, while in other conditions, also considered, its basin of attraction consists of a countable number of closed and bounded intervals, whose length tends to zero. A detailed study of the second iteration of the function shows that under these conditions of the parameters there are not 2-cycles. By numerical simulation of the system, illustrations of the respective bifurcation diagrams and basins of attraction are obtained. A good match with the analytical results is achieved. Key Word — Bifurcation, stability, fixed point, dynamical systems.
pyInfinityFlow: optimized imputation and analysis of high-dimensional flow cytometry data for millions of cells

Abstract Motivation While conventional flow cytometry is limited to dozens of markers, new experimental and computational strategies, such as Infinity Flow, allow for the generation and imputation of hundreds of cell surface protein markers in millions of cells. Here, we describe an end-to-end analysis workflow for Infinity Flow data in Python. Results pyInfinityFlow enables the efficient analysis of millions of cells, without down-sampling, through direct integration with well-established Python packages for single-cell genomics analysis. pyInfinityFlow accurately identifies both common and extremely rare cell populations which are challenging to define from single-cell genomics studies alone. We demonstrate that this workflow can nominate novel markers to design new flow cytometry gating strategies for predicted cell populations. pyInfinityFlow can be extended to diverse cell discovery analyses with flexibility to adapt to diverse Infinity Flow experimental designs. Availability and implementation pyInfinityFlow is freely available in GitHub (https://github.com/KyleFerchen/pyInfinityFlow) and on PyPI (https://pypi.org/project/pyInfinityFlow/). Package documentation with tutorials on a test dataset is available by Read the Docs (pyinfinityflow.readthedocs.io). The scripts and data for reproducing the results are available at https://github.com/KyleFerchen/pyInfinityFlow/tree/main/analysis_scripts, along with the raw flow cytometry input data.
Membrane protein isolation and structure determination in cell-derived membrane vesicles

Significance Structural studies of membrane proteins historically have relied almost exclusively on first removing membrane proteins from the membrane with dispersive agents like detergents. But dispersive agents destabilize many membrane proteins, and in all membrane proteins, they remove weakly associated lipids, cofactors, and proteins essential to normal biological function. The procedures presented in this paper allow structure determination at atomic resolution without ever removing the proteins from their membrane environment. Thus, the structures of proteins that heretofore could not be extracted stably from the membrane can now be analyzed, and weakly associated lipid molecules, cofactors, and proteins normally lost in isolation are retained. These methods have immediately become the approach of choice for membrane protein structure determination in our laboratory.
Discovery of a bifunctional xylanolytic enzyme with arabinoxylan arabinofuranohydrolase‐d3 and endo‐xylanase activities and its application in the hydrolysis of cereal arabinoxylans

Xylanolytic enzymes, with both endo‐xylanase and arabinoxylan arabinofuranohydrolase (AXH) activities, are attractive for the economically feasible conversion of recalcitrant arabinoxylan. However, their characterization and utilization of these enzymes in biotechnological applications have been limited. Here, we characterize a novel bifunctional enzyme, rAbf43A, cloned from a bacterial consortium that exhibits AXH and endo‐xylanase activities. Hydrolytic pattern analyses revealed that the AXH activity belongs to AXHd3 because it attacked only the C(O)‐3‐linked arabinofuranosyl residues of double‐substituted xylopyranosyl units of arabinoxylan and arabinoxylan‐derived oligosaccharides, which are usually resistant to hydrolysis. The enzyme rAbf43A also liberated a series of xylo‐oligosaccharides (XOSs) from beechwood xylan, xylohexaose and xylopentaose, indicating that rAbf43A exhibited endo‐xylanase activity. Homology modelling based on AlphaFold2 and site‐directed mutagenesis identified three non‐catalytic residues (H161, A270 and L505) located in the substrate‐binding pocket essential for its dual‐functionality, while the mutation of A117 located in the −1 subsite to the proline residue only affected its endo‐xylanase activity. Additionally, rAbf43A showed significant synergistic action with the bifunctional xylanase/feruloyl esterase rXyn10A/Fae1A from the same bacterial consortium on insoluble wheat arabinoxylan and de‐starched wheat bran degradation. When rXyn10A/Fae1A was added to the rAbf43A pre‐hydrolyzed reactions, the amount of released reducing sugars, xylose and ferulic acid increased by 9.43% and 25.16%, 189.37% and 93.54%, 31.39% and 32.30%, respectively, in comparison with the sum of hydrolysis products released by each enzyme alone. The unique characteristics of rAbf43A position it as a promising candidate not only for designing high‐performance enzyme cocktails but also for investigating the structure–function relationship of GH43 multifunctional enzymes.
Studies of Streptococcus anginosus Virulence in Dictyostelium discoideum and Galleria mellonella Models

For many years, Streptococcus anginosus has been considered a commensal colonizing the oral cavity, as well as the gastrointestinal and genitourinary tracts. However, recent epidemiological and clinical data designate this bacterium as an emerging opportunistic pathogen. ABSTRACT For many years, Streptococcus anginosus has been considered a commensal colonizing the oral cavity, as well as the gastrointestinal and genitourinary tracts. However, recent epidemiological and clinical data designate this bacterium as an emerging opportunistic pathogen. Despite the reported pathogenicity of S. anginosus, the molecular mechanism underpinning its virulence is poorly described. Therefore, our goal was to develop and optimize efficient and simple infection models that can be applied to examine the virulence of S. anginosus and to study host-pathogen interactions. Using 23 S. anginosus isolates collected from different infections, including severe and superficial infections, as well as an attenuated strain devoid of CppA, we demonstrate for the first time that Dictyostelium discoideum is a suitable model for initial, fast, and large-scale screening of virulence. Furthermore, we found that another nonvertebrate animal model, Galleria mellonella, can be used to study the pathogenesis of S. anginosus infection, with an emphasis on the interactions between the pathogen and host innate immunity. Examining the profile of immune defense genes, including antimicrobial peptides, opsonins, regulators of nodulation, and inhibitors of proteases, by quantitative PCR (qPCR) we identified different immune response profiles depending on the S. anginosus strain. Using these models, we show that S. anginosus is resistant to the bactericidal activity of phagocytes, a phenomenon confirmed using human neutrophils. Notably, since we found that the data from these models corresponded to the clinical severity of infection, we propose their further application to studies of the virulence of S. anginosus.