Miracle-dz/UniFAR-FTUs huggingface dataset:
P2X7 Receptor and Purinergic Signaling: Orchestrating Mitochondrial Dysfunction in Neurodegenerative Diseases

Visual Abstract Mitochondrial dysfunction is one of the basic hallmarks of cellular pathology in neurodegenerative diseases. Since the metabolic activity of neurons is highly dependent on energy supply, nerve cells are especially vulnerable to impaired mitochondrial function. Besides providing oxidative phosphorylation, mitochondria are also involved in controlling levels of second messengers such as Ca2+ ions and reactive oxygen species (ROS). Interestingly, the critical role of mitochondria as producers of ROS is closely related to P2XR purinergic receptors, the activity of which is modulated by free radicals. Here, we review the relationships between the purinergic signaling system and affected mitochondrial function. Purinergic signaling regulates numerous vital biological processes in the CNS. The two main purines, ATP and adenosine, act as excitatory and inhibitory neurotransmitters, respectively. Current evidence suggests that purinergic signaling best explains how neuronal activity is related to neuronal electrical activity and energy homeostasis, especially in the development of Alzheimer’s and Parkinson’s diseases. In this review, we focus on the mechanisms underlying the involvement of the P2RX7 purinoreceptor in triggering mitochondrial dysfunction during the development of neurodegenerative disorders. We also summarize various avenues by which the purine signaling pathway may trigger metabolic dysfunction contributing to neuronal death and the inflammatory activation of glial cells. Finally, we discuss the potential role of the purinergic system in the search for new therapeutic approaches to treat neurodegenerative diseases.
The matrix equations $XA-AX=X^{\alpha}g(X)$ over fields or rings

Let $n,\alpha\geq 2$. Let $K$ be an algebraically closed field with characteristic $0$ or greater than $n$. We show that the dimension of the variety of pairs $(A,B)\in {M_n(K)}^2$, with $B$ nilpotent, that satisfy $AB-BA=A^{\alpha}$ or $A^2-2AB+B^2=0$ is $n^2-1$ ; moreover such matrices $(A,B)$ are simultaneously triangularizable. Let $R$ be a reduced ring such that $n! $ is not a zero-divisor and $A$ be a generic matrix over $R$ ; we show that $X=0$ is the sole solution of $AX-XA=X^{\alpha}$. Let $R$ be a commutative ring with unity ; let $A$ be similar to $\mathrm{diag}(\lambda_1I_{n_1},\cdots,\lambda_rI_{n_r})$ such that, for every $i\not= j$, $\lambda_i-\lambda_j$ is not a zero-divisor. If $X$ is a nilpotent solution of $XA-AX=X^{\alpha}g(X)$ where $g$ is a polynomial, then $AX=XA$.
On type-II singularities in Ricci flow on $\mathbb{R}^{N}$

In each dimension $N\geq 3$ and for each real number $\lambda\geq 1$, we construct a family of complete rotationally symmetric solutions to Ricci flow on $\mathbb{R}^{N}$ which encounter a global singularity at a finite time $T$. The singularity forms arbitrarily slowly with the curvature blowing up arbitrarily fast at the rate $(T-t)^{-(\lambda+1)}$. Near the origin, blow-ups of such a solution converge uniformly to the Bryant soliton. Near spatial infinity, blow-ups of such a solution converge uniformly to the shrinking cylinder soliton. As an application of this result, we prove that there exist standard solutions of Ricci flow on $\mathbb{R}^N$ whose blow-ups near the origin converge uniformly to the Bryant soliton.
Association of Biological Age with Tumor Microenvironment in Patients with Esophageal Adenocarcinoma

Biological age-related adaptations have been shown to modulate the non-malignant cells comprising the tumor microenvironment (TME). In the current work, we studied the association between biological age and TME characteristics in patients with esophageal adenocarcinoma. We comparatively assessed intratumoral histologic stroma quantity, tumor immune cell infiltrate, and blood leukocyte and thrombocyte count in 72 patients stratified in 3 strata of biological age (younger <70 years, fit older [≥]70 years, and frail older adults [≥]70 years), as defined by a geriatric assessment. Frailty in older adults was predictive of decreased intratumoral stroma quantity (B = -14.66% stroma, P = 0.022) relative to tumors in chronological-age-matched fit older adults. Moreover, in comparison to younger adults, frail older adults (P = 0.032), but not fit older adults (P = 0.302), demonstrated a lower blood thrombocyte count at the time of diagnosis. Lastly, we found an increased proportion of tumors with a histologic desert TME phenotype in frail older adults. Our findings provide a biological underpinning for the clinical relevance of assessing frailty in patients with esophageal adenocarcinoma, further justifying the need for standardized geriatric assessment in geriatric cancer patients.
beta-functions in higher dimensional field theories

We review recent activity in the construction of the renormalization group functions for O(N) scalar and gauge theories in six and higher dimensions. The theories lie in their respective universality classes at the Wilson-Fisher fixed point. The critical exponents at this fixed point in the various dimensions are all in agreement with the known exponents determined in the large Nexpansion.
$Ab$ $initio$ low-energy effective Hamiltonians for high-temperature superconducting cuprates Bi$_2$Sr$_2$CuO$_6$, Bi$_2$Sr$_2$CaCu$_2$O$_8$, HgBa$_2$CuO$_4$ and CaCuO$_2$

We derive ab initio low-energy eﬀective Hamiltonians (LEH) for high-temperature superconducting (SC) copper oxides Bi 2 Sr 2 CuO 6 (Bi2201, N (cid:96) = 1, T exp c ∼ 10 K), Bi 2 Sr 2 CaCu 2 O 8 (Bi2212, N (cid:96) = 2, T exp c ∼ 84 K), HgBa 2 CuO 4 (Hg1201, N (cid:96) = 1, T exp c ∼ 90 K) and CaCuO 2 (Ca11, N (cid:96) = ∞ , T exp c ∼ 110 K), with diﬀerent experimental optimal SC transition temperature T exp c and number N (cid:96) of laminated CuO 2 planes between the two neighboring block layers. We apply the latest methodology of the multiscale ab initio scheme for correlated electron systems (MACE), and focus on the LEH consisting of one antibonding (AB) Cu3 d x 2 − y 2 /O2 p σ orbital centered on each Cu atom. We discuss prominent features of this LEH: (1) The ratio U/ | t 1 | between the onsite eﬀective Coulomb repulsion (ECR) U and amplitude of nearest neighbour hopping t 1 increases with T exp c and N (cid:96) , consistently with the expected increase in d -wave SC correlation function P dd with U/ | t 1 | . One possible cause of the increase of U/ | t 1 | is the replacement of apical O atoms by Cu atoms from neighbouring CuO 2 planes when N (cid:96) increases. Furthermore, we show that the increase in distance between Cu and apical O atoms decreases the eﬀective screening (ES) by electrons outside of the LEH and increases U/ | t 1 | . (2) For Hg1201 and Ca11, we show that U/ | t 1 | decreases when hole doping per AB orbital δ increases, which may partly account for the disappearance of SC when δ exceeds the optimal value in experiment. (3) For N (cid:96) ≥ 2, oﬀ-site inter-CuO 2 plane ECR is comparable to oﬀ-site intra-CuO 2 plane ECR. We discuss contributions of inter-CuO 2 plane ECR to both P dd and the stability of the SC state.
Local Magnification for Data and Feature Augmentation

In recent years, many data augmentation techniques have been proposed to increase the diversity of input data and reduce the risk of overfitting on deep neural networks. In this work, we propose an easy-to-implement and model-free data augmentation method called Local Magnification (LOMA). Different from other geometric data augmentation methods that perform global transformations on images, LOMA generates additional training data by randomly magnifying a local area of the image. This local magnification results in geometric changes that significantly broaden the range of augmentations while maintaining the recognizability of objects. Moreover, we extend the idea of LOMA and random cropping to the feature space to augment the feature map, which further boosts the classification accuracy considerably. Experiments show that our proposed LOMA, though straightforward, can be combined with standard data augmentation to significantly improve the performance on image classification and object detection. And further combination with our feature augmentation techniques, termed LOMA_IF&FO, can continue to strengthen the model and outperform advanced intensity transformation methods for data augmentation.
Far-from-equilibrium universality in two-dimensional Heisenberg antiferromagnets

We study the far-from-equilibrium dynamics of isolated two-dimensional Heisenberg antiferromagnets. We consider spin spiral initial conditions which imprint a position-dependent staggered-magnetization (or Neel order) in the two-dimensional lattice. Remarkably, we find a long-lived prethermal regime characterized by self-similar behavior of staggered magnetization fluctuations, although the system has no long-range order at finite energy and the staggered magnetization does not couple with conserved charges. Exploiting the separation of length scales introduced by the initial condition, we derive a simplified analytical model that allow us to compute the spatial-temporal scaling exponents and power-law distribution of the staggered magnetization fluctuations analytically, and find excellent agreement with numerical simulations using phase space methods. The scaling exponents are insensitive to details of the initial condition, in particular, no fine-tuning of energy is required to trigger the self-similar scaling regime. Compared with recent results on far-from-equilibrium universality on the Heisenberg ferromagnet, we find quantitatively distinct spatial-temporal scaling exponents, therefore suggesting that the same model with ferromagnetic and antiferromagnetic initial conditions can host different universal regimes. Our predictions are relevant to ultra-cold atoms simulators of Heisenberg magnets and driven antiferromagnetic insulators.
X-Volt: Joint Tuning of Driver Strengths and Supply Voltages Against Power Side-Channel Attacks

Power side-channel (PSC) attacks are well-known threats to sensitive hardware like advanced encryption standard (AES) crypto cores. Given the significant impact of supply voltages (VCCs) on power profiles, various countermeasures based on VCC tuning have been proposed, among other defense strategies. Driver strengths of cells, however, have been largely overlooked, despite having direct and significant impact on power profiles as well. For the first time, we thoroughly explore the prospects of jointly tuning driver strengths and VCCs as novel working principle for PSC-attack countermeasures. Toward this end, we take the following steps: 1) we develop a simple circuit-level scheme for tuning; 2) we implement a CAD flow for design-time evaluation of ASICs, enabling security assessment of ICs before tape-out; 3) we implement a correlation power analysis (CPA) framework for thorough and comparative security analysis; 4) we conduct an extensive experimental study of a regular AES design, implemented in ASIC as well as FPGA fabrics, under various tuning scenarios; 5) we summarize design guidelines for secure and efficient joint tuning. In our experiments, we observe that runtime tuning is more effective than static tuning, for both ASIC and FPGA implementations. For the latter, the AES core is rendered > 11.8x (i.e., at least 11.8 times) as resilient as the untuned baseline design. Layout overheads can be considered acceptable, with, e.g., around +10% critical-path delay for the most resilient tuning scenario in FPGA. We release source codes for our methodology, as well as artifacts from the experimental study in[13].
On the Biological Plausibility of Orthogonal Initialisation for Solving Gradient Instability in Deep Neural Networks

Initialising the synaptic weights of artificial neural networks (ANNs) with orthogonal matrices is known to alleviate vanishing and exploding gradient problems. A major objection against such initialisation schemes is that they are deemed biologically implausible as they mandate factorization techniques that are difficult to attribute to a neurobiological process. This paper presents two initialisation schemes that allow a network to naturally evolve its weights to form orthogonal matrices, provides theoretical analysis that pre-training orthogonalisation always converges, and empirically confirms that the proposed schemes outperform randomly initialised recurrent and feedforward networks.
The Aleksandrov-Bakelman-Pucci estimate and the Calabi-Yau equation

We give two applications of the Aleksandrov-Bakelman-Pucci estimate to the Calabi-Yau equation on symplectic four-manifolds. The first is solvability of the equation on the Kodaira-Thurston manifold for certain almost-Kahler structures assuming $S^1$-invariance, extending a result of Buzano-Fino-Vezzoni. The second is to reduce the general case of Donaldson's conjecture to a bound on the measure of a superlevel set of a scalar function.
Nucleon structure with two flavors of dynamical domain-wall fermions

We present a numerical lattice quantum chromodynamics calculation of isovector form factors and the first few moments of the isovector structure functions of the nucleon. The calculation employs two degenerate dynamical flavors of domain-wall fermions, resulting in good control of chiral symmetry breaking. Nonperturbative renormalization of the relevant quark currents is performed where necessary. The DBW2 gauge action is used to further improve the chiral behavior while maintaining a reasonable physical lattice volume. The inverse lattice spacing, a{sup -1}, is approximately 1.7 GeV. Degenerate up and down dynamical quark masses of approximately 1, 3/4 and 1/2 times the strange quark mass are used. The physical volume of the lattice is about (1.9 fm){sup 3}. The ratio of the isovector vector to axial charges, g{sub A}/g{sub V}, tends to a lower value than the experimental value as the quark mass is reduced toward the physical point. Momentum-transfer dependences of the isovector vector, axial, induced-tensor and induced-pseudoscalar form factors are calculated. The Goldberger-Treiman relation holds at low momentum transfer and yields an estimation of the pion-nucleon coupling, g{sub {pi}}{sub NN}=15.5(1.4), where the quoted error is only statistical. We find that the flavor nonsinglet quark momentum fraction {sub u-d} and quark helicity fractionmore » {sub {delta}}{sub u-{delta}}{sub d} overshoot their experimental values after linear chiral extrapolation. We discuss possible systematic errors for this discrepancy. An estimate for transversity, {sub {delta}}{sub u-{delta}}{sub d}=0.93(6) in MS at 2 GeV, is obtained and a twist-3 polarized moment, d{sub 1}, appears small, suggesting that the Wandzura-Wilczek relation holds approximately. We discuss in detail the systematic errors in the calculation, with particular attention paid to finite volume, excited-state contamination, and chiral extrapolations. « less
Separating paths systems of almost linear size

A separating path system for a graph $G$ is a collection $\mathcal{P}$ of paths in $G$ such that for every two edges $e$ and $f$ in $G$, there is a path in $\mathcal{P}$ that contains $e$ but not $f$. We show that every $n$-vertex graph has a separating path system of size $O(n \log^* n)$. This improves upon the previous best upper bound of $O(n \log n)$, and makes progress towards a conjecture of Falgas-Ravry--Kittipassorn--Kor\'andi--Letzter--Narayanan and Balogh--Csaba--Martin--Pluh\'ar, according to which an $O(n)$ bound should hold.
Star formation rates on global and cloud scales within the Galactic Centre

Abstract The environment within the inner few hundred parsecs of the Milky Way, known as the “Central Molecular Zone” (CMZ), harbours densities and pressures orders of magnitude higher than the Galactic Disc; akin to that at the peak of cosmic star formation (Kruijssen & Longmore 2013). Previous studies have shown that current theoretical star-formation models under-predict the observed level of star-formation (SF) in the CMZ by an order of magnitude given the large reservoir of dense gas it contains. Here we explore potential reasons for this apparent dearth of star formation activity.
A Photoconductor Intrinsically Has No Gain

In the past 50 years, the high gain in quantum efficiency of photoconductors is often explained by a widely accepted theory in which the photogain is proportional to the minority carrier lifetime and inversely proportional to the carrier transit time across the photoconductor. It occasionally misleads scientists to believe that a high-speed and high-gain photodetector can be made simply by shortening the device length. The theory is derived on the assumption that the distribution of photogenerated excess carriers is spatially uniform. In this Letter, we find that this assumption is not valid for a photoconductive semiconductor due to the metal-semiconductor boundary at the two metal electrodes inducing carrier confinement. By solving the continuity equation and performing numerical simulations, we conclude that a photoconductor intrinsically has no gain or at least no high gain, no matter how short the transit time and how long the minority lifetime is. The high gain observed in experiments comes from other extrinsic effects such as defects, surface states and surface depletion regions that localize excess minority carriers, leaving a large number of excess majority carriers accumulated in the conduction channel for the photogain. Following the Ohm's Law, a universal equation governing the photogain in a photoconductor is established at the end of this Letter.
Deep Temporal Modelling of Clinical Depression through Social Media Text

We describe the development of a model to detect user-level clinical depression based on a user's temporal social media posts. Our model uses a Depression Symptoms Detection (DSD) classifier, which is trained on the largest existing samples of clinician annotated tweets for clinical depression symptoms. We subsequently use our DSD model to extract clinically relevant features, e.g., depression scores and their consequent temporal patterns, as well as user posting activity patterns, e.g., quantifying their ``no activity'' or ``silence. '' Furthermore, to evaluate the efficacy of these extracted features, we create three kinds of datasets including a test dataset, from two existing well-known benchmark datasets for user-level depression detection. We then provide accuracy measures based on single features, baseline features and feature ablation tests, at several different levels of temporal granularity. The relevant data distributions and clinical depression detection related settings can be exploited to draw a complete picture of the impact of different features across our created datasets. Finally, we show that, in general, only semantic oriented representation models perform well. However, clinical features may enhance overall performance provided that the training and testing distribution is similar, and there is more data in a user's timeline. The consequence is that the predictive capability of depression scores increase significantly while used in a more sensitive clinical depression detection settings.
Lipschitz type, radial growth and Dirichlet type spaces on functions induced by certain elliptic operators

In this paper, we investigate properties of classes of functions related to certain elliptic operators. Firstly, we prove that a main result of Dyakonov (Acta Math. 178(1997), 143--167) on analytic functions can be extended to this more general setting. Secondly, we study the radial growth on these functions and the obtained results are generalizations of the corresponding results of Makarov (Proc. London Math. Soc. 51(1985), 369--384) and Korenblum (Bull. Amer. Math. Soc. 12(1985), 99--102). Finally, we discuss the Dirichlet type energy integrals on such classes of functions and their applications.
HGV4Risk: Hierarchical Global View-guided Sequence Representation Learning for Risk Prediction

Risk prediction, usually achieved by learning representations from patient’s physiological sequence or user’s behavioral sequence data, and has been widely applied in healthcare and finance. Despite that, some recent time-aware deep learning methods have led to superior performances in such sequence representation learning tasks, such improvement is limited due to a lack of guidance from hierarchical global view. To address this issue, we propose a novel end-to-end Hierarchical Global View-guided (HGV) sequence representation learning framework. Specifically, the Global Graph Embedding (GGE) module is proposed to learn sequential clip-aware representations from temporal correlation graph (TCG) at instance level. Furthermore, following the way of key-query attention, the harmonic β-attention (β-Attn) is also developed for making a global tradeoff between time-aware decay and observation significance at channel level adaptively. Moreover, the hierarchical representations at both instance level and channel level can be coordinated by the heterogeneous information aggregation under the guidance of global view. Experimental results on both healthcare risk prediction benchmark and SMEs credit overdue risk prediction task from the real-world industrial scenario in MYBank, Ant Group, have illustrated that the proposed model can achieve competitive prediction performance compared with other known baselines. The code has been released public available at: https://github.com/LiYouru0228/HGV.
Byzantine Spectral Ranking

We study the problem of rank aggregation where the goal is to obtain a global ranking by aggregating pair-wise comparisons of voters over a set of items. We consider an adversarial setting where the voters are partitioned into two sets. The first set votes in a stochastic manner according to the popular score-based Bradley-Terry-Luce (BTL) model for pairwise comparisons. The second set comprises malicious Byzantine voters trying to deteriorate the ranking. We consider a strongly-adversarial scenario where the Byzantine voters know the BTL scores, the votes of the good voters, the algorithm, and can collude with each other. We first show that the popular spectral ranking based Rank-Centrality algorithm, though optimal for the BTL model, does not perform well even when a small constant fraction of the voters are Byzantine. We introduce the Byzantine Spectral Ranking Algorithm (and a faster variant of it), which produces a reliable ranking when the number of good voters exceeds the number of Byzantine voters. We show that no algorithm can produce a satisfactory ranking with probability>1/2 for all BTL weights when there are more Byzantine voters than good voters, showing that our algorithm works for all possible population fractions. We support our theoretical results with experimental results on synthetic and real datasets to demonstrate the failure of the Rank-Centrality algorithm under several adversarial scenarios and how the proposed Byzantine Spectral Ranking algorithm is robust in obtaining good rankings.
When to Use What: An In-Depth Comparative Empirical Analysis of OpenIE Systems for Downstream Applications

Open Information Extraction (OpenIE) has been used in the pipelines of various NLP tasks. Unfortunately, there is no clear consensus on which models to use in which tasks. Muddying things further is the lack of comparisons that take differing training sets into account. In this paper, we present an application-focused empirical survey of neural OpenIE models, training sets, and benchmarks in an effort to help users choose the most suitable OpenIE systems for their applications. We find that the different assumptions made by different models and datasets have a statistically significant effect on performance, making it important to choose the most appropriate model for one’s applications. We demonstrate the applicability of our recommendations on a downstream Complex QA application.