Miracle-dz/UniFAR-FTUs huggingface dataset:
Canstatin,a novel matrix-derived inhibitor of angiogenesis and tumor growth

Canstatin, a C terminal fragment of the α 2 chain of type Ⅳ collagen, 24000 molecule weight, was found by Kamphaus in 1999. As a human basement membrane derived inhibitor of angiogenesis and tumor growth, Canstatin has been paid great attention since it was isolated and identified. Canstatin significantly inhibited human endothelial cell migration, proliferation and murine endothelial cell tube formation. Canstatin also suppress in vivo growth of large and small size tumors in two human xenograft mouse models. Collectively, canstatin might be a powerful therapeutic molecule for suppressing angiogenesis and tumor growth and metastasis.
TRIER: Template-Guided Neural Networks for Robust and Interpretable Sleep Stage Identification from EEG Recordings

Neural networks often obtain sub-optimal representations during training, which degrade robustness as well as classification performances. This is a severe problem in applying deep learning to bio-medical domains, since models are vulnerable to being harmed by irregularities and scarcities in data. In this study, we propose a pre-training technique that handles this challenge in sleep staging tasks. Inspired by conventional methods that experienced physicians have used to classify sleep states from the existence of characteristic waveform shapes, or template patterns, our method introduces a cosine similarity based convolutional neural network to extract representative waveforms from training data. Afterwards, these features guide a model to construct representations based on template patterns. Through extensive experiments, we demonstrated that guiding a neural network with template patterns is an effective approach for sleep staging, since (1) classification performances are significantly enhanced and (2) robustness in several aspects are improved. Last but not least, interpretations on models showed that notable features exploited by trained experts are correctly addressed during prediction in the proposed method.
Partial substitution of bean (Phaseolus vulgaris) flour for fishmeal in extruded diets for rainbow trout (Oncorhynchus mykiss): Effects on yield parameters

The objective of this research was to evaluate yield parameters (gained weight, weight percentage, survival, feed conversion factor (FCR), feed conversion efficiency (FCE), condition factor (K), specific growth rate (SGR) and hepatosomatic index (HSI) of trouts fed with experimental diets elaborated with bean (Phaseolus vulgaris L.) flour instead of fishmeal with 15, 30 and 45% (BF15, BF30 and BF45, respectively) for 32 days, as well as a control diet (CD). The greatest weight gain was presented by fish fed with BF15 and BF30 (14.48 and 14.14 g, respectively) with no significant differences (p>0.05) and an approximate increase of 50% of their initial weight. FCR did not show significant differences (p>0.05) among CD, BF15 and BF30 diets with an average value of 2.05. FCE did not show significant differences (p>0.05) between diets BF15 and BF30 with an average value of 46.70%. SGR did not show significant differences (p>0.05) between BF15 and BF30 diets with an average value of 1.25. It is concluded that 30% is the maximum substitution without causing a decrease in yield and nutritional parameters in rainbow trout under the experiment conditions, although further research is suggested.
Quantum State Complexity in Computationally Tractable Quantum Circuits

Characterizing the quantum complexity of local random quantum circuits is a very deep problem with implications to the seemingly disparate fields of quantum information theory, quantum many-body physics and high energy physics. While our theoretical understanding of these systems has progressed in recent years, numerical approaches for studying these models remains severely limited. In this paper, we discuss a special class of numerically tractable quantum circuits, known as quantum automaton circuits, which may be particularly well suited for this task. These are circuits which preserve the computational basis, yet can produce highly entangled output wave functions. Using ideas from quantum complexity theory, especially those concerning unitary designs, we argue that automaton wave functions have high quantum state complexity. We look at a wide variety of metrics, including measurements of the output bit-string distribution and characterization of the generalized entanglement properties of the quantum state, and find that automaton wave functions closely approximate the behavior of fully Haar random states. In addition to this, we identify the generalized out-of-time ordered 2k-point correlation functions as a particularly useful probe of complexity in automaton circuits. Using these correlators, we are able to numerically study the growth of complexity well beyond the scrambling time for very large systems. As a result, we are able to present evidence of a linear growth of design complexity in local quantum circuits, consistent with conjectures from quantum information theory.
GTEA: Representation Learning for Temporal Interaction Graphs via Edge Aggregation

We consider the problem of representation learning for temporal interaction graphs where a network of entities with complex interactions over an extended period of time is modeled as a graph with a rich set of node and edge attributes. In particular, an edge between a node-pair within the graph corresponds to a multi-dimensional time-series. To fully capture and model the dynamics of the network, we propose GTEA, a framework of representation learning for temporal interaction graphs with per-edge time-based aggregation. Under GTEA, a Graph Neural Network (GNN) is integrated with a state-of-the-art sequence model, such as LSTM, Transformer and their time-aware variants. The sequence model generates edge embeddings to encode temporal interaction patterns between each pair of nodes, while the GNN-based backbone learns the topological dependencies and relationships among different nodes. GTEA also incorporates a sparsity-inducing self-attention mechanism to distinguish and focus on the more important neighbors of each node during the aggregation process. By capturing temporal interactive dynamics together with multi-dimensional node and edge attributes in a network, GTEA can learn fine-grained representations for a temporal interaction graph to enable or facilitate other downstream data analytic tasks. Experimental results show that GTEA outperforms state-of-the-art schemes including GraphSAGE, APPNP, and TGAT by delivering higher accuracy (100.00%, 98.51%, 98.05% ,79.90%) and macro-F1 score (100.00%, 98.51%, 96.68% ,79.90%) over four large-scale real-world datasets for binary/ multi-class node classification.
The structure of Lie algebras with a derivation satisfying a polynomial identity

Abstract We prove nilpotency results for Lie algebras over an arbitrary field admitting a derivation, which satisfies a given polynomial identity r(t) = 0. In the special case of the polynomial we obtain a uniform bound on the nilpotency class of Lie algebras admitting a periodic derivation of order n. We even find an optimal bound on the nilpotency class in characteristic p if p does not divide a certain invariant ρn .
Reforms Meet Fairness Concerns In School And College Admissions

Recently, many matching systems around the world have been reformed. These reforms responded to objections that the matching mechanisms in use were unfair and manipulable. Surprisingly, the mechanisms remained unfair even after the reforms: the new mechanisms may induce an outcome with a blocking student who desires and deserves a school which she did not receive. However, as we show in this paper, the reforms introduced matching mechanisms which are more fair compared to the counterfactuals. First, most of the reforms introduced mechanisms that are more fair by stability: whenever the old mechanism does not have a blocking student, the new mechanism does not have a blocking student either. Second, some reforms introduced mechanisms that are more fair by counting: the old mechanism always has at least as many blocking students as the new mechanism. These findings give a novel rationale to the reforms and complement the recent literature showing that the same reforms have introduced less manipulable matching mechanisms. We further show that the fairness and manipulability of the mechanisms are strongly logically related.
Extending Label Smoothing Regularization with Self-Knowledge Distillation

Inspired by the strong correlation between the Label Smoothing Regularization(LSR) and Knowledge distillation(KD), we propose an algorithm LsrKD for training boost by extending the LSR method to the KD regime and applying a softer temperature. Then we improve the LsrKD by a Teacher Correction(TC) method, which manually sets a constant larger proportion for the right class in the uniform distribution teacher. To further improve the performance of LsrKD, we develop a self-distillation method named Memory-replay Knowledge Distillation (MrKD) that provides a knowledgeable teacher to replace the uniform distribution one in LsrKD. The MrKD method penalizes the KD loss between the current model's output distributions and its copies' on the training trajectory. By preventing the model learning so far from its historical output distribution space, MrKD can stabilize the learning and find a more robust minimum. Our experiments show that LsrKD can improve LSR performance consistently at no cost, especially on several deep neural networks where LSR is ineffectual. Also, MrKD can significantly improve single model training. The experiment results confirm that the TC can help LsrKD and MrKD to boost training, especially on the networks they are failed. Overall, LsrKD, MrKD, and their TC variants are comparable to or outperform the LSR method, suggesting the broad applicability of these KD methods.
Embodied Visual Navigation With Automatic Curriculum Learning in Real Environments

We present NavACL, a method of automatic curriculum learning tailored to the navigation task. NavACL is simple to train and efficiently selects relevant tasks using geometric features. In our experiments, deep reinforcement learning agents trained using NavACL significantly outperform state-of-the-art agents trained with uniform sampling – the current standard. Furthermore, our agents can navigate through unknown cluttered indoor environments to semantically-specified targets using only RGB images. Obstacle-avoiding policies and frozen feature networks support transfer to unseen real-world environments, without any modification or retraining requirements. We evaluate our policies in simulation, and in the real world on a ground robot and a quadrotor drone. Videos of real-world results are available in the supplementary material.
Canonical variational completion of 4D Gauss-Bonnet gravity

A proposal to obtain a finite contribution of second derivative order to the gravitational field equations in (D = 4) dimensions from a renormalized Gauss-Bonnet term in the action has recently received a wave of attention, and triggered a discussion whether the employed renormalization procedure yields a well-defined theory. One of the main criticisms is based on the fact that the resulting field equations cannot be obtained as the Euler-Lagrange equations from a diffeomorphism-invariant action. In this work we use techniques from the inverse calculus of variation as an independent confirmation that the suggested truncated Gauss-Bonnet field equations cannot be variational, in any dimension. For this purpose, we employ canonical variational completion, based on the notion of Vainberg-Tonti Lagrangian, which consists in adding a canonically defined correction term to a given system of equations, so as to make them derivable from an action. We find that in $D>4$ the suggested field equations can be variationally completed, which yields a theory with fourth order field equations. In $D=4$ the variationally completed theory diverges. Our findings are in line with Lovelock's theorem, which states that, in 4 dimensions, the unique second-order Euler-Lagrange equations arising from a scalar density depending on the metric tensors and its derivatives, are the Einstein equations with a cosmological constant.
Physically Embedded Planning Problems: New Challenges for Reinforcement Learning

Recent work in deep reinforcement learning (RL) has produced algorithms capable of mastering challenging games such as Go, chess, or shogi. In these works the RL agent directly observes the natural state of the game and controls that state directly with its actions. However, when humans play such games, they do not just reason about the moves but also interact with their physical environment. They understand the state of the game by looking at the physical board in front of them and modify it by manipulating pieces using touch and fine-grained motor control. Mastering complicated physical systems with abstract goals is a central challenge for artificial intelligence, but it remains out of reach for existing RL algorithms. To encourage progress towards this goal we introduce a set of physically embedded planning problems and make them publicly available. We embed challenging symbolic tasks (Sokoban, tic-tac-toe, and Go) in a physics engine to produce a set of tasks that require perception, reasoning, and motor control over long time horizons. Although existing RL algorithms can tackle the symbolic versions of these tasks, we find that they struggle to master even the simplest of their physically embedded counterparts. As a first step towards characterizing the space of solution to these tasks, we introduce a strong baseline that uses a pre-trained expert game player to provide hints in the abstract space to an RL agent's policy while training it on the full sensorimotor control task. The resulting agent solves many of the tasks, underlining the need for methods that bridge the gap between abstract planning and embodied control.
Alzheimers Disease: A Novel Hypothesis Integrating Spirochetes, Biofilm, and the Immune System

In the light of recent studies showing the presence of spirochetes in the brains of Alzheimer’s disease (AD) patients, we have studied (post mortem) the hippocampus region in the brains of similarly affected AD patients utilizing both pathology and immunohistochemistry. Our findings demonstrate that the plaques, which are characteristically found in AD brains, reveal the presence of biofilms. These biofilms are undoubtedly made by the spirochetes present there; further, we have also found that the biofilms co-localize with the β amyloid that is a signature finding in the disease. Also, we have shown activation of Toll-like receptor 2 in the same areas. We postulate this is related to the disease because this innate immune system molecule cannot penetrate the biofilm to destroy the spirochetes present there, so, inasmuch as it is activated, it destroys the surrounding tissue instead. We compare this destruction to that which is caused by activation of the adaptive immune system, which leads to much more severe devastation, much more rapidly.
End-to-End Optimization of Coherent Optical Communications Over the Split-Step Fourier Method Guided by the Nonlinear Fourier Transform Theory

Optimizing modulation and detection strategies for a given channel is critical to maximizing the throughput of a communication system. Such an optimization can be easily carried out analytically for channels that admit closed-form analytical models. However, this task becomes extremely challenging for nonlinear dispersive channels such as the optical fiber. End-to-end optimization through autoencoders (AEs) can be applied to define symbol-to-waveform (modulation) and waveform-to-symbol (detection) mappings, but so far it has been mainly shown for systems relying on approximate channel models. Here, for the first time, we propose an AE scheme applied to the full optical channel described by the nonlinear Schrödinger equation (NLSE). Transmitter and receiver are jointly optimized through the split-step Fourier method (SSFM) which accurately models an optical fiber. In this first numerical analysis, the detection is performed by a neural network (NN), whereas the symbol-to-waveform mapping is aided by the nonlinear Fourier transform (NFT) theory in order to simplify and guide the optimization on the modulation side. This proof-of-concept AE scheme is thus benchmarked against a manually-optimized NFT-based system and a three-fold increase in achievable distance (from 2000 to 6640 km) is demonstrated.
Subspecific Taxonomy of Ficedula parva Based on Sequences of Mitochondrial Cytochrome b Gene

It was disputed that Ficedula parva parva and F.p.albicilla were two species or two subspecies.Complete sequences of mitochondrial cytochrome b gene were used as a genetic marker to study the taxonomic status of them.The genetic distance between F.p.albicilla and F.p.parva was 6 3% based on the Kimura's two Parameter's method,which was far more than the genetic distance between other species of Ficedula . The results suggest that F.p.albicilla and F.p.parva should be two species,their names are F.albicilla and F.parva separately,and the divergence time between F.p.albicilla and F.p.parva is 3 15 to 3 25 My.
Interplay between spatial anisotropy and next-nearest-neighbor exchange interactions in the triangular Heisenberg model

We investigate the interplay between spatial anisotropy and further exchange interactions in the spin-$\frac{1}{2}$ Heisenberg antiferromagnetic model on a triangular lattice. We use the Schwinger boson theory by including Gaussian fluctuations above the mean-field approach. The phase diagram exhibits a strong reduction of the long range collinear and incommensurate spirals regions with respect to the mean-field ones. This reduction is accompanied by the emergence of its short range order counterparts, leaving an ample room for $0$-flux and nematic spin liquid regions. Remarkably, within the neighborhood of the spatially isotropic line, there is a range where the spirals are so fragile that only the commensurate $120^{\circ}$ Neel ones survive. The good agreement with recent variational Monte Carlo predictions gives support to the rich phase diagram induced by spatial anisotropy.
Stellar Populations of a Sample of Optically Selected AGN-host Dwarf Galaxies

In this paper we present our studies on the stellar populations and star formation histories (SFHs) for the Reines et al. sample of 136 dwarf galaxies that host active galactic nuclei (AGNs), selected from the Sloan Digital Sky Survey Data Release 8. We derive stellar populations and reconstruct SFHs for these AGN-host dwarfs using the stellar population synthesis code STARLIGHT. Our results suggest that these AGN-host dwarfs have assembled their stellar masses within a narrow period of time with the stellar mass-weighted ages in the range of 109–1010 yr, but show a wide diversity of SFHs with the luminosity-weighted stellar ages in the range of 107–1010 yr. The old population (t > 109 yr) contributes most to the galaxy light for the majority of the sample; the young population (t < 108 yr) also appears in significant but widely varying fractions, while the intermediate-age population (108 < t < 109 yr) in general contributes less to the optical continuum at 4020 Å. We also find that these dwarfs follow a similar mass–metallicity relation to normal star-forming galaxies, indicating that AGNs have little effect on the chemical evolution of the host galaxy. We further investigate the relation between the derived SFHs and morphology of the host galaxy, and find no correlation. Comparing the SFHs with the luminosity of the [O iii] λ5007 line ( ), we find a mild correlation exists when erg s−1, indicating a physical connection between star formation and AGN activities in these dwarf galaxies.
Searching for the Kardar-Parisi-Zhang phase in microcavity polaritons

Recent analytical work has shown that, at certain values of the external pump, the optical parametric oscillator (OPO) regime of microcavity polaritons may provide a realisation of Kardar-Parisi-Zhang (KPZ) physics in 2D. Here, we verify this by solving the full microscopic model numerically using the truncated Wigner method, and studying the first order spatial correlations. For the predicted pump strengths, these correlations decay much faster and, perpendicular to the pump, fit closely to the stretched exponential form predicted by the KPZ equation, in contrast to the usual algebraic decay. This strongly indicates the viability of observing KPZ behaviour in future polariton OPO experiments.
Young Faithful: The Eruptions of EC 53 as It Cycles through Filling and Draining the Inner Disk

While young stellar objects sometimes undergo bursts of accretion, these bursts usually occur sporadically, making them challenging to study observationally and to explain theoretically. We build a schematic description of cyclical bursts of the young stellar object EC 53 (also known as V371 Ser) using near-IR and submillimeter monitoring obtained over six cycles, each lasting ≈530 days. EC 53 brightens over 0.12 yr by 0.3 mag at 850 μm, by 2 mag at 3.35 μm, and by 1.5 mag at near-IR wavelengths, to a maximum luminosity consistent with an accretion rate of ∼8 × 10−6 M⊙ yr−1. The emission then decays with an e-folding timescale of until the accretion rate is ∼1 × 10−6 M⊙ yr−1. The next eruption then occurs, likely triggered by the buildup of a ∼5 × 10−6 M⊙ mass in the inner disk, enough for it to become unstable and drain onto the star. Just before outburst, when the disk is almost replenished, the near-IR colors become redder, indicating an increase in the geometrical height of the disk by this mass buildup. The reddening disappears soon after the initial burst, as much of the mass is drained from the disk. We quantify physical parameters related to the accretion process in EC 53 by assuming an α-disk formulation, constrained by the observed disk properties and accretion rate. While we can only speculate about the possible trigger for these faithful eruptions, we hope that our quantified schematic will motivate theorists to test the hypothesized mechanisms that could cause the cyclical buildup and draining of mass in the inner disk.
Fungal Systematics and Evolution

A smut fungus that hinders wiregrass restoration efforts in longleaf pine-grassland ecosystems was collected from Aristida stricta and A. beyrichiana (Poaceae) in three states in the southeastern USA. Morphological and phylogenetic characteristics of this fungus were examined. These data show that the specimens from both plant species were infected by the same fungus and represent a new species of Langdonia. The new species differs morphologically from other species of Langdonia by teliospores being solitary and not compacted into spore balls. Spore wall ornamentation and teliospore size also differ from other Langdonia species. Phylogenetic analyses of DNA sequences of the ITS, LSU, and EF-1α supported separation of the species from A. stricta and A. beyrichiana from other Langdonia species. Based on these results, a new species, Langdonia walkerae, is proposed.