Miracle-dz/UniFAR-FTUs huggingface dataset:
Demonstration of structural alterations in experimental corneal infectious model using multiphoton microscopy

The aim of this study is to assess the application of multiphoton autofluorescence and second harmonic generation (SHG) microscopy for investigating the structural alterations and the pattern of microbial spreading during corneal infectious process in an in vitro organ culture model. The autofluorescence spectrum derived from pathogens allows us to monitoring the pattern of microbial spreading within corneal lamellae. In addition, the destruction and regeneration of second harmonic generating collagen during infectious process can also be monitored in a non-invasive fashion. Therefore we propose that multiphoton microscopy may potentially be applied as an effective monitoring tool for corneal infection studies.
Negative self-regulation of transient receptor potential canonical 4 by the specific interaction with phospholipase C-δ1

Transient receptor potential canonical (TRPC) channels are non-selective calcium-permeable cation channels. It is suggested that TRPC4β is regulated by phospholipase C (PLC) signaling and is especially maintained by phosphatidylinositol 4,5-bisphosphate (PIP2). In this study, we present the regulation mechanism of the TRPC4 channel with PIP2 hydrolysis which is mediated by a channel-bound PLCδ1 but not by the GqPCR signaling pathway. Our electrophysiological recordings demonstrate that the Ca2+ via an open TRPC4 channel activates PLCδ1 in the physiological range, and it causes the decrease of current amplitude. The existence of PLCδ1 accelerated PIP2 depletion when the channel was activated by an agonist. Interestingly, PLCδ1 mutants which have lost the ability to regulate PIP2 level failed to reduce the TRPC4 current amplitude. Our results demonstrate that TRPC4 self-regulates its activity by allowing Ca2+ ions into the cell and promoting the PIP2 hydrolyzing activity of PLCδ1.
Rational singularities of higher dimensional schemes

Two examples of rational singularities of schemes over an algebraically closed field of characteristic zero are given: Singularities occurring as the quotient of a regular scheme by a finite group and singularities of the type u2 v2-g(t. tN). Unless otherwise explicitly mentioned, we assume that all schemes are irreducible, reduced and of finite type over an algebraically closed field k of characteristic zero and that all points are k-rational. Let U be a scheme, X a regular scheme and g: X -U a proper, surjective, birational morphism. We call g: X -U a resolution of U. DEFINITION 1. Let Y be a scheme and y E Y. Then Y has a rational singularity at y if there exists a neighbourhood U of y in Y, such that for every resolution g: X-> U we have g*9x-t ? u and Rig*? 9x = 0 for i =# 0 (henceforth we write Rg Cx? u) In this paper we want to discuss two examples of rational singularities needed in [5, ? 5]. REMARKS. (i) Using "flat base change" [1] it is easy to see that the question whether y E Y is a rational singularity depends only on ? 9 r ("y " denotes the completion with respect to the maximal ideal). (ii) If the base field has positive characteristic, one needs additional conditions to define rational singularities [3]. (iii) Every regular point of a scheme is a rational singularity [2]. (iv) In Definition 1 it is sufficient to consider one resolution of U. This last statement follows from (iii) and the Leray spectral sequence. Using the same kind of argument one gets LEMMA 1. Let h: Y'-Y be a proper, surjective, birational morphism of schemes. Assume that Y' has only rational singularities; then Rh*c9y _ ? if and only if Y has only rational singularities. We first consider quotient singularities: DEFINITION 2. Let Y be a scheme and y E Y. Then Y has a quotient singularity at y if there exist a regular scheme Y' and a finite group G acting Received by the editors June 17, 1976. AMS (MOS) subject classifications (1970). Primary 14E15, 14B05; Secondary 14E20.
Trend and Co-occurrence Network of COVID-19 Symptoms From Large-Scale Social Media Data: Infoveillance Study

Background For an emergent pandemic, such as COVID-19, the statistics of symptoms based on hospital data may be biased or delayed due to the high proportion of asymptomatic or mild-symptom infections that are not recorded in hospitals. Meanwhile, the difficulty in accessing large-scale clinical data also limits many researchers from conducting timely research. Objective Given the wide coverage and promptness of social media, this study aimed to present an efficient workflow to track and visualize the dynamic characteristics and co-occurrence of symptoms for the COVID-19 pandemic from large-scale and long-term social media data. Methods This retrospective study included 471,553,966 COVID-19–related tweets from February 1, 2020, to April 30, 2022. We curated a hierarchical symptom lexicon for social media containing 10 affected organs/systems, 257 symptoms, and 1808 synonyms. The dynamic characteristics of COVID-19 symptoms over time were analyzed from the perspectives of weekly new cases, overall distribution, and temporal prevalence of reported symptoms. The symptom evolutions between virus strains (Delta and Omicron) were investigated by comparing the symptom prevalence during their dominant periods. A co-occurrence symptom network was developed and visualized to investigate inner relationships among symptoms and affected body systems. Results This study identified 201 COVID-19 symptoms and grouped them into 10 affected body systems. There was a significant correlation between the weekly quantity of self-reported symptoms and new COVID-19 infections (Pearson correlation coefficient=0.8528; P<.001). We also observed a 1-week leading trend (Pearson correlation coefficient=0.8802; P<.001) between them. The frequency of symptoms showed dynamic changes as the pandemic progressed, from typical respiratory symptoms in the early stage to more musculoskeletal and nervous symptoms in the later stages. We identified the difference in symptoms between the Delta and Omicron periods. There were fewer severe symptoms (coma and dyspnea), more flu-like symptoms (throat pain and nasal congestion), and fewer typical COVID symptoms (anosmia and taste altered) in the Omicron period than in the Delta period (all P<.001). Network analysis revealed co-occurrences among symptoms and systems corresponding to specific disease progressions, including palpitations (cardiovascular) and dyspnea (respiratory), and alopecia (musculoskeletal) and impotence (reproductive). Conclusions This study identified more and milder COVID-19 symptoms than clinical research and characterized the dynamic symptom evolution based on 400 million tweets over 27 months. The symptom network revealed potential comorbidity risk and prognostic disease progression. These findings demonstrate that the cooperation of social media and a well-designed workflow can depict a holistic picture of pandemic symptoms to complement clinical studies.
Using Colors and Sketches to Count Subgraphs in a Streaming Graph

Suppose we wish to estimate $\#H$, the number of copies of some small graph $H$ in a large streaming graph $G$. There are many algorithms for this task when $H$ is a triangle, but just a few that apply to arbitrary $H$. Here we focus on one such algorithm, which was introduced by Kane, Mehlhorn, Sauerwald, and Sun. The storage and update time per edge for their algorithm are both $O(m^k/(\#H)^2)$, where $m$ is the number of edges in $G$, and $k$ is the number of edges in $H$. Here, we propose three modifications to their algorithm that can dramatically reduce both the storage and update time. Suppose that $H$ has no leaves and that $G$ has maximum degree $\leq m^{1/2 - \alpha}$, where $\alpha>0$. Define $C = \min(m^{2\alpha},m^{1/3})$. Then in our version of the algorithm, the update time per edge is $O(1)$, and the storage is approximately reduced by a factor of $C^{2k-t-2}$, where $t$ is the number of vertices in $H$; in particular, the storage is $O(C^2 + m^k/(C^{2k-t-2} (\#H)^2))$.
Controlled and Conditional Text to Image Generation with Diffusion Prior

Denoising Diffusion models have shown remarkable performance in generating diverse, high quality images from text. Numerous techniques have been proposed on top of or in alignment with models like Stable Diffusion and Imagen that generate images directly from text. A lesser explored approach is DALLE-2's two step process comprising a Diffusion Prior that generates a CLIP image embedding from text and a Diffusion Decoder that generates an image from a CLIP image embedding. We explore the capabilities of the Diffusion Prior and the advantages of an intermediate CLIP representation. We observe that Diffusion Prior can be used in a memory and compute efficient way to constrain the generation to a specific domain without altering the larger Diffusion Decoder. Moreover, we show that the Diffusion Prior can be trained with additional conditional information such as color histogram to further control the generation. We show quantitatively and qualitatively that the proposed approaches perform better than prompt engineering for domain specific generation and existing baselines for color conditioned generation. We believe that our observations and results will instigate further research into the diffusion prior and uncover more of its capabilities.
Shadows and photon rings of regular black holes and geonic horizonless compact objects

The optical appearance of a body compact enough to feature an unstable bound orbit, when surrounded by an accretion disk, is expected to be dominated by a luminous ring of radiation enclosing a central brightness depression typically known as the shadow. Despite observational limitations, the rough details of this picture have been now confirmed by the results of the Event Horizon Telescope (EHT) Collaboration on the imaging of the M87 and Milky Way supermassive central objects. However, the precise characterization of both features—ring and shadow—depends on the interaction between the background geometry and the accretion disk, thus being a fertile playground to test our theories on the nature of compact objects and the gravitational field itself in the strong-field regime. In this work we use both features in order to test a continuous family of solutions interpolating between regular black holes and horizonless compact objects, which arise within the Eddington-inspired Born–Infeld theory of gravity, a viable extension of Einstein’s general relativity (GR). To this end we consider seven distinctive classes of such configurations (five black holes and two traversable wormholes) and study their optical appearances under illumination by a geometrically and optically thin accretion disk, emitting monochromatically with three analytic intensity profiles previously suggested in the literature. We build such images and consider the sub-ring structure created by light rays crossing the disk more than once and existing on top of the main ring of radiation. We discuss in detail the modifications as compared to their GR counterparts, the Lyapunov exponents of unstable nearly-bound orbits, as well as the differences between black hole and traversable wormholes for the three intensity profiles. In addition we use the claim by the EHT Collaboration on the radius of the bright ring acting (under proper calibrations) as a proxy for the radius of the shadow itself to explore the parameter space of our solutions compatible with such a result.
Dynamic Averaging Load Balancing on Arbitrary Graphs

In this paper we study dynamic averaging load balancing on general graphs. We consider infinite time and dynamic processes, where in every step new load items are assigned to randomly chosen nodes. A matching is chosen, and the load is averaged over the edges of that matching. We analyze the discrete case where load items are indivisible, moreover our results also carry over to the continuous case where load items can be split arbitrarily. For the choice of the matchings we consider three different models, random matchings of linear size, random matchings containing only single edges, and deterministic sequences of matchings covering the whole graph. We bound the discrepancy, which is defined as the difference between the maximum and the minimum load. Our results cover a broad range of graph classes and, to the best of our knowledge, our analysis is the first result for discrete and dynamic averaging load balancing processes. As our main technical contribution we develop a drift result that allows us to apply techniques based on the effective resistance in an electrical network to the setting of dynamic load balancing.
Even-odd paired dispersion for shocks with two-sided oscillations: the (modified) Korteweg-de Vries equation

Our curiosity of the structural nature of the dynamics was motivated by two observations: mathemati-cally, the variational principle and Hamiltonian formulation of some models, such as the Korteweg-de Vries (KdV) equation, are preserved, mutatis mutandis , if each mode of freedom is assigned a different dispersion coeﬀicient; and, physically, some oscillations on both sides of some ion-acoustic and quantum shocks. We thus propose re-grouping and re-assignment, particularly the distinguishment and pairing of opposite signs for alternative Fourier components, of the dispersions for different dynamical modes. The KdV equation with periodic boundary condition and longest-wave sinusoidal initial field, as used by N. Zabusky and M. D. Kruskal, is chosen for our case study with such pairing of the Fourier modes of (normalized) even and odd wavenumbers. Numerical results verify the two-sided oscillations around the shock but also indicate even more, including non-convergence to the classical shock (described by the entropy solution), non-thermalization and applicability to other models including the modified KdV equation with cubic nonlinearity. A unification of various dispersive models, keeping the essential mathematical elegance of each, for phenomena with complicated dispersion relation is thus suggested.
VLSP2022-EVJVQA Challenge: Multilingual Visual Question Answering

Visual Question Answering (VQA) is a challenging task of natural language processing (NLP) and computer vision (CV), attracting significant attention from researchers. English is a resource-rich language that has witnessed various developments in datasets and models for visual question answering. Visual question answering in other languages also would be developed for resources and models. In addition, there is no multilingual dataset targeting the visual content of a particular country with its own objects and cultural characteristics. To address the weakness, we provide the research community with a benchmark dataset named EVJVQA, including 33,000+ pairs of question-answer over three languages: Vietnamese, English, and Japanese, on approximately 5,000 images taken from Vietnam for evaluating multilingual VQA systems or models. EVJVQA is used as a benchmark dataset for the challenge of multilingual visual question answering at the 9th Workshop on Vietnamese Language and Speech Processing (VLSP 2022). This task attracted 62 participant teams from various universities and organizations. In this article, we present details of the organization of the challenge, an overview of the methods employed by shared-task participants, and the results. The highest performances are 0.4392 in F1-score and 0.4009 in BLUE on the private test set. The multilingual QA systems proposed by the top 2 teams use ViT for the pre-trained vision model and mT5 for the pre-trained language model, a powerful pre-trained language model based on the transformer architecture. EVJVQA is a challenging dataset that motivates NLP and CV researchers to further explore the multilingual models or systems for visual question answering systems.
The Geometry of Mixability

Mixable loss functions are of fundamental importance in the context of prediction with expert advice in the online setting since they characterize fast learning rates. By re-interpreting properness from the point of view of differential geometry, we provide a simple geometric characterization of mixability for the binary and multi-class cases: a proper loss function $\ell$ is $\eta$-mixable if and only if the superpredition set $\textrm{spr}(\eta \ell)$ of the scaled loss function $\eta \ell$ slides freely inside the superprediction set $\textrm{spr}(\ell_{\log})$ of the log loss $\ell_{\log}$, under fairly general assumptions on the differentiability of $\ell$. Our approach provides a way to treat some concepts concerning loss functions (like properness) in a ''coordinate-free'' manner and reconciles previous results obtained for mixable loss functions for the binary and the multi-class cases.
A Scalable Space-efficient In-database Interpretability Framework for Embedding-based Semantic SQL Queries

AI-Powered database (AI-DB) is a novel relational database system that uses a self-supervised neural network, database embedding, to enable semantic SQL queries on relational tables. In this paper, we describe an architecture and implementation of in-database interpretability infrastructure designed to provide simple, transparent, and relatable insights into ranked results of semantic SQL queries supported by AI-DB. We introduce a new co-occurrence based interpretability approach to capture relationships between relational entities and describe a space-efficient probabilistic Sketch implementation to store and process co-occurrence counts. Our approach provides both query-agnostic (global) and query-specific (local) interpretabilities. Experimental evaluation demonstrate that our in-database probabilistic approach provides the same interpretability quality as the precise space-inefficient approach, while providing scalable and space efficient runtime behavior (up to 8X space savings), without any user intervention.
Finding a Small Vertex Cut on Distributed Networks

We present an algorithm for distributed networks to efficiently find a small vertex cut in the CONGEST model. Given a positive integer κ, our algorithm can, with high probability, either find κ vertices whose removal disconnects the network or return that such κ vertices do not exist. Our algorithm takes κ3· Õ(D+√n) rounds, where n is the number of vertices in the network and D denotes the network’s diameter. This implies Õ(D+√n) round complexity whenever κ=polylog(n). Prior to our result, a bound of Õ(D) is known only when κ=1,2 [Parter, Petruschka DISC’22]. For κ≥ 3, this bound can be obtained only by an O(logn)-approximation algorithm [Censor-Hillel, Ghaffari, Kuhn PODC’14], and the only known exact algorithm takes O((κΔ D)O(κ)) rounds, where Δ is the maximum degree [Parter DISC’19]. Our result answers an open problem by Nanongkai, Saranurak, and Yingchareonthawornchai [STOC’19].
Stability analysis of T-S fuzzy control systems using parameter-dependent Lyapunov function

The system stability of T–S fuzzy-model-based control systems with a parameter-dependent Lyapunov function (PDLF) is investigated. As PDLF approach includes information of the membership function (time derivatives of membership functions), it has been reported that relaxed stability conditions can be achieved compared to the parameter-independent Lyapunov function (PILF). To investigate the system stability, the non-linear plant is represented by a T–S fuzzy model. Various non-parallel distribution compensation (PDC) fuzzy controllers, which can better utilise the characteristic of the PDLF, are proposed to close the feedback loop. To relax the stability conditions, an improved PDLF is employed. Some inequalities are proposed to relate the membership functions and its time derivatives, which allow the introduction of some slack matrices to facilitate the stability analysis. Stability conditions in terms of linear matrix inequalities are derived to aid the design of stable fuzzy-model-based control systems. Simulation examples are given to illustrate the effectiveness of the proposed non-PDC fuzzy control schemes.
Mitigating Unnecessary Handovers in Ultra-Dense Networks through Machine Learning-based Mobility Prediction

In 5G wireless communication, Intelligent Transportation Systems (ITS) and automobile applications, such as autonomous driving, are widely examined. These applications have strict requirements and often require high Quality of Service (QoS). In an urban setting, Ultra-Dense Networks (UDNs) have the potential to not only provide optimal QoS but also increase system capacity and frequency reuse. However, the current architecture of 5G UDN of dense Small Cell Nodes (SCNs) deployment prompts increased delay, handover times, and handover failures. In this paper, we propose a Machine Learning (ML) supported Mobility Prediction (MP) strategy to predict future Vehicle User Equipment (VUE) mobility and handover locations. The primary aim of the proposed methodology is to minimize Unnecessary Handover (UHO) to ensure reliable communication in vehicular communication systems. We evaluate and validate our approach on a downlink system-level simulator. We predict mobility using Support Vector Machine (SVM), Decision Tree Classifier (DTC), and Random Forest Classifier (RFC). Simulation results demonstrate a notable 30% decrease in handover times and a considerable enhancement in QoS, maintaining the advantage of 5G UDN networks.
Outlook on Magnetohydrodynamical Turbulence and Its Astrophysical Implications

Magnetohydrodynamical (MHD) turbulence is ubiquitous in magnetized astrophysical plasmas, and it radically changes a great variety of astrophysical processes. In this review, we introduce the concept of MHD turbulence and explain the origin of its scaling. We consider the implications of MHD turbulence for various problems: dynamo in different types of stars, flare activity, solar and stellar wind from different stars, the propagation of cosmic rays, and star formation. We also discuss how the properties of MHD turbulence provide a new means of tracing magnetic fields in interstellar and intracluster media.
Online Minimum Spanning Trees with Weight Predictions

We consider the minimum spanning tree problem with predictions, using the weight-arrival model, i.e., the graph is given, together with predictions for the weights of all edges. Then the actual weights arrive one at a time and an irrevocable decision must be made regarding whether or not the edge should be included into the spanning tree. In order to assess the quality of our algorithms, we define an appropriate error measure and analyze the performance of the algorithms as a function of the error. We prove that, according to competitive analysis, the simplest algorithm, Follow-the-Predictions, is optimal. However, intuitively, one should be able to do better, and we present a greedy variant of Follow-the-Predictions. In analyzing that algorithm, we believe we present the first random order analysis of a non-trivial online algorithm with predictions, by which we obtain an algorithmic separation. This may be useful for distinguishing between algorithms for other problems when Follow-the-Predictions is optimal according to competitive analysis.
On general multi-quadratic function field extensions in the GHS attack

To date, elliptic curves offer the most efficient cryptographic solution. Particularly efficient among elliptic curves, are those defined over binary composite finite fields, such as GF ((2 r ) n ). These curves were no longer considered secure when, in 1998, Gerhard Frey innovated a concept which paved the road for the GHS attack. The idea behind the GHS attack is to map the Discrete Logarithm Problem (DLP) over such a curve to an equivalent DLP over the jacobian of another curve, defined over the smaller field GF (2 r ). In this thesis, we study the theoretical structure of the GHS attack for elliptic curves defined over fields of arbitrary characteristics. We study the GHS attack using general quadratic extensions for elliptic curves defined over composite fields of even characteristic and we estimate the genus of resulting function field. We also implement the GHS attack and present some computational results. Keywords . GHS Attack, Elliptic Curve Cryptography, Function Fields