Miracle-dz/UniFAR-FTUs huggingface dataset:
Alternation of Magnetic Anisotropy Accompanied by Metal-Insulator Transition in Strained Ultrathin Manganite Heterostructures

Fundamental understanding of interfacial magnetic properties in ferromagnetic heterostructures is essential to utilize ferromagnetic materials for spintronic device applications. In this paper, we investigate the interfacial magnetic and electronic structures of epitaxial single-crystalline LaAlO$_3$ (LAO)/La$_{0.6}$Sr$_{0.4}$MnO$_3$ (LSMO)/Nb:SrTiO$_3$ (Nb:STO) heterostructures with varying LSMO-layer thickness, in which the magnetic anisotropy strongly changes depending on the LSMO thickness due to the delicate balance between the strains originating from both the Nb:STO and LAO layers, using x-ray magnetic circular dichroism (XMCD) and photoemission spectroscopy (PES). We successfully detect the clear change of the magnetic behavior of the Mn ions concomitant with the thickness-dependent metal-insulator transition (MIT). Our results suggest that double-exchange interaction induces the ferromagnetism in the metallic LSMO film under tensile strain caused by the SrTiO$_3$ substrate, while superexchange interaction determines the magnetic behavior in the insulating LSMO film under compressive strain originating from the top LAO layer. Based on those findings, the formation of a magnetic dead layer near the LAO/LSMO interface is attributed to competition between the superexchange interaction via Mn 3$d_{3z^2-r^2}$ orbitals under compressive strain and the double-exchange interaction via the 3$d_{x^2-y^2}$ orbitals.
A Survey of DDOS Attacks and Solutions using Packet Filtering Mechanism

Distributed Denial Of Service attack is the most significant and continuous threat in cyber security. It is one of the biggest security concerns for security professionals and has taken the attention of today’s cyber world. This attack is an attempt by an attacker to flood the victim machine by generating a large volume of traffic using compromised machines. DDOS attacks can affect any device on any network and at the same time target different types of resources such as CPU, memory, and bandwidth, etc. The Decentralized nature of the internet helped the attacker to lunch this type of attack. The impact of a DDOS attack is huge like Money, time, clients and even reputation of the organization can be lost. Depending on the severity of an attack, resources could be offline for 24 hours, multiple days, or even a week. To prevent this attack, it is very important to filter the attack traffic not to enter the network. All filtering techniques are applied to the routers which ensure that only legitimate traffic can get access to a system. In this paper, we provide an overview of different types of DDOS attacks. we also cover different packet filtering techniques found in the literature along with their success and failure in DDOS prevention.
The Character Map in Nonabelian Cohomology

We extend the Chern character on K-theory, in its generalization to the Chern-Dold character on generalized cohomology theories, further to (twisted, differential) non-abelian cohomology theories, where its target is a non-abelian de Rham cohomology of twisted L-infinity algebra valued differential forms. The construction amounts to leveraging the fundamental theorem of dg-algebraic rational homotopy theory to a twisted non-abelian generalization of the de Rham theorem. We show that the non-abelian character reproduces, besides the Chern-Dold character, also the Chern-Weil homomorphism as well as its secondary Cheeger-Simons homomorphism on (differential) non-abelian cohomology in degree 1, represented by principal bundles (with connection); and thus generalizes all these to higher (twisted, differential) non-abelian cohomology, represented by higher bundles/higher gerbes (with higher connections). As a fundamental example, we discuss the twisted non-abelian character map on twistorial Cohomotopy theory over 8-manifolds, which can be viewed as a twisted non-abelian enhancement of topological modular forms (tmf) in degree 4. This turns out to exhibit a list of subtle topological relations that in high energy physics are thought to govern the charge quantization of fluxes in M-theory.
Effect of nitrogen application rate on physiology of flue-cured tobacco plants with or without inoculation of Phytophthora parasitica var. nicotianae and the severity of black shank

The effect of different nitrogen(N) application rates(N 71,142,213 and 284 mg/kg) on polyphenol oxidase(PPO) and catalase(CAT) activity and the contents of phenols,flavonoids,soluble sugars,free amino acids,and total nitrogen,as well as the incidence and severity of black shank in flue-cured tobacco(Nicotiana tabacum L.cv.Hongda) plants,was studied in a pot culture experiment with or without inoculation of the black shank pathogen(Phytophthora parasitica var.nicotianae). The results showed that inoculation of black shank pathogen led to higher PPO and CAT activities,and higher contents of phenols,flavonoids,soluble sugars,free amino acids,and total nitrogen in the tobacco plants 5 and 15 days after inoculation(DAI) compared to non-inoculated plants.A similar tendency was found under the different N application rates.Increasing N amount caused higher contents of N and free amino acids,but decreased contents of flavonoids,phenols and soluble sugars and lowered CAT activity,in both inoculated and non-inoculated tobacco plants 0,5 and 15 DAI.The highest and lowest nitrogen amounts induced significant differences in the above-mentioned biochemical indices.Activity of PPO was higher in the N 142 and 213 mg/kg treatments than in the N 71 and 284 mg/kg treatments.In addition,increased severity of black shank was observed with increasing N application rate.Overall,the results indicated that N application rate affected not only the initial resistance(pre-infection of the pathogen),but also the induced resistance(post-infection),which in turn resulted in the different occurrences of black shank.Optimal N management is recommended to decrease the severity of black shank in the commercial production of N.tabacum cv.Hongda.
Fractional vertex-arboricity of planar graphs

We initiate a systematic study of the fractional vertex-arboricity of planar graphs and demonstrate connections to open problems concerning both fractional coloring and the size of the largest induced forest in planar graphs. In particular, the following three long-standing conjectures concern the size of a largest induced forest in a planar graph, and we conjecture that each of these can be generalized to the setting of fractional vertex-arboricity. In 1979, Albertson and Berman conjectured that every planar graph has an induced forest on at least half of its vertices, in 1987, Akiyama and Watanabe conjectured that every bipartite planar graph has an induced forest on at least five-eighths of its vertices, and in 2010, Kowalik, Lužar, and Skrekovski conjectured that every planar graph of girth at least five has an induced forest on at least seven-tenths of its vertices. We make progress toward the fractional generalization of the latter of these, by proving that every planar graph of girth at least five has fractional vertex-arboricity at most $2 - 1/324$.
The positive sentinel lymph node biopsy- can we predict which patients will benefit from further surgery?

Introduction: Although sentinel lymph node biopsy (SLNB) is the gold standard for clinical and radiological negative axillae in breast cancer, the subsequent management of positive nodes is currently under scrutiny with the Z0011 trial publication causing much debate. Our aim was to determine whether there were any predictive factors in our cohort of patients that could assist in the decision of whether the patient required a further axillary lymph node clearance. Methods: A retrospective analysis of a prospectively maintained database of patients who underwent SLNB and their histology was performed. Univariate and multivariate analysis was performed to identify any predictive factors. Results: Our Breast Unit performed 457 SLNB over the three years. The mean age of patients was 61.9 years (range 31-89 years). Of these 457 SLNB, 122 (26.7%) were positive for metastatic involvement, and of these patients only 34% were found to have further lymph node involvement after axillary lymph node clearance (ALNC). Only 8% of our total patient cohort had non sentinel node involvement at completion ALNC. Using univariate analysis, lymphovascular invasion (p0.009), grade (p0.007) and size (p0.006) were all significant predictors for having a positive ALNC. Conclusion: Only 8% of the total number of patients had an additional positive lymph node found in their completion axillary clearance. Our results add to this existing knowledge on the subject, and show that there are certain factors which should be carefully considered pre-operatively in order to help inform patient and surgeon choice regarding management of the axilla.
Hadronic X-Ray Flares from Blazars

The detection of a high-energy neutrino from the flaring blazar TXS 0506+056 and the subsequent discovery of a neutrino excess from the same direction have strengthened the hypothesis that blazars are cosmic neutrino sources. The lack, however, of γ-ray flaring activity during the latter period challenges the standard scenario of correlated γ-ray and high-energy neutrino emission in blazars. We propose instead that TeV–PeV neutrinos are produced in coincidence with X-ray flares that are powered by proton synchrotron radiation. In this case, neutrinos are produced by photomeson interactions of protons with their own synchrotron radiation, while MeV to GeV γ-rays are the result of synchrotron-dominated electromagnetic cascades developed in the source. Using a time-dependent approach, we find that this “pure hadronic flaring” hypothesis has several interesting consequences. The X-ray flux is a good proxy for the all-flavor neutrino flux, while certain neutrino-rich X-ray flares may be dark in GeV–TeV γ-rays. Lastly, hadronic X-ray flares are accompanied by an equally bright MeV component that is detectable by proposed missions like e-ASTROGAM and AMEGO. We applied this scenario to the extreme blazar 3HSP J095507.9+355101 which has been associated with IceCube-200107A while undergoing an X-ray flare. We showed that the number of muon and anti-muon neutrinos above 100 TeV during hadronic flares can be up to ∼3–10 times higher than the expected number in standard leptohadronic models. Still, frequent hadronic flaring activity is necessary for explaining the detected neutrino event IceCube-200107A.
Spatial-Temporal Demand Forecasting and Competitive Supply via Graph Convolutional Networks

We consider a setting with an evolving set of requests for transportation from an origin to a destination before a deadline and a set of agents capable of servicing the requests. In this setting, an assignment authority is to assign agents to requests such that the average idle time of the agents is minimized. An example is the scheduling of taxis (agents) to meet incoming requests for trips while ensuring that the taxis are empty as little as possible. In this paper, we study the problem of spatial-temporal demand forecasting and competitive supply (SOUP). We address the problem in two steps. First, we build a granular model that provides spatial-temporal predictions of requests. Specifically, we propose a Spatial-Temporal Graph Convolutional Sequential Learning (ST-GCSL) algorithm that predicts the service requests across locations and time slots. Second, we provide means of routing agents to request origins while avoiding competition among the agents. In particular, we develop a demand-aware route planning (DROP) algorithm that considers both the spatial-temporal predictions and the supplydemand state. We report on extensive experiments with realworld and synthetic data that offer insight into the performance of the solution and show that it is capable of outperforming the state-of-the-art proposals.
Weighted Model Counting in FO2 with Cardinality Constraints and Counting Quantifiers: A Closed Form Formula

Weighted First-Order Model Counting (WFOMC) computes the weighted sum of the models of a first-order logic theory on a given finite domain. First-Order Logic theories that admit polynomial-time WFOMC w.r. t domain cardinality are called domain liftable. We introduce the concept of lifted interpretations as a tool for formulating closed forms for WFOMC. Using lifted interpretations, we reconstruct the closed-form formula for polynomial-time FOMC in the universally quantified fragment of FO2, earlier proposed by Beame et al. We then expand this closed-form to incorporate cardinality constraints, existential quantifiers, and counting quantifiers (a.k.a C2) without losing domain-liftability. Finally, we show that the obtained closed-form motivates a natural definition of a family of weight functions strictly larger than symmetric weight functions.
Deep Multi-Scale Feature Learning for Defocus Blur Estimation

This paper presents an edge-based defocus blur estimation method from a single defocused image. We first distinguish edges that lie at depth discontinuities (called depth edges, for which the blur estimate is ambiguous) from edges that lie at approximately constant depth regions (called pattern edges, for which the blur estimate is well-defined). Then, we estimate the defocus blur amount at pattern edges only, and explore an interpolation scheme based on guided filters that prevents data propagation across the detected depth edges to obtain a dense blur map with well-defined object boundaries. Both tasks (edge classification and blur estimation) are performed by deep convolutional neural networks (CNNs) that share weights to learn meaningful local features from multi-scale patches centered at edge locations. Experiments on naturally defocused images show that the proposed method presents qualitative and quantitative results that outperform state-of-the-art (SOTA) methods, with a good compromise between running time and accuracy.
Network geometry and market instability

The complexity of financial markets arise from the strategic interactions among agents trading stocks, which manifest in the form of vibrant correlation patterns among stock prices. Over the past few decades, complex financial markets have often been represented as networks whose interacting pairs of nodes are stocks, connected by edges that signify the correlation strengths. However, we often have interactions that occur in groups of three or more nodes, and these cannot be described simply by pairwise interactions but we also need to take the relations between these interactions into account. Only recently, researchers have started devoting attention to the higher-order architecture of complex financial systems, that can significantly enhance our ability to estimate systemic risk as well as measure the robustness of financial systems in terms of market efficiency. Geometry-inspired network measures, such as the Ollivier–Ricci curvature and Forman–Ricci curvature, can be used to capture the network fragility and continuously monitor financial dynamics. Here, we explore the utility of such discrete Ricci curvatures in characterizing the structure of financial systems, and further, evaluate them as generic indicators of the market instability. For this purpose, we examine the daily returns from a set of stocks comprising the USA S&P-500 and the Japanese Nikkei-225 over a 32-year period, and monitor the changes in the edge-centric network curvatures. We find that the different geometric measures capture well the system-level features of the market and hence we can distinguish between the normal or ‘business-as-usual’ periods and all the major market crashes. This can be very useful in strategic designing of financial systems and regulating the markets in order to tackle financial instabilities.
Topological phases in the 
α−Li3N
-type crystal structure of light-element compounds

Materials with tunable topological features, simple crystal structure and flexible synthesis, are in extraordinary demand towards technological exploitation of unique properties of topological nodal points. The controlled design of the lattice geometry of light elements is determined by utilizing density functional theory and the effective Hamiltonian model together with the symmetry analysis. This provides an intriguing venue for reasonably achieving various distinct types of novel fermions. We, therefore, show that a nodal line (type-I and II), Dirac fermion, and triple point (TP) fermionic excitation can potentially appear as a direct result of a band inversion in group-I nitrides with $\alpha$-Li$_{\rm 3}$N-type crystal structure. The imposed strain is exclusively significant for these compounds, and it invariably leads to the considerable modification of the nodal line type. Most importantly, a type-II nodal loop can be realized in the system under strain. These unique characteristics make $\alpha$-Li$_{\rm 3} $N-type crystal structure an ideal playground to achieve various types of novel fermions well-suited for technological applications.
A Comprehensive Survey of the Tactile Internet: State-of-the-Art and Research Directions

The Internet has made several giant leaps over the years, from a fixed to a mobile Internet, then to the Internet of Things, and now to a Tactile Internet. The Tactile Internet goes far beyond data, audio and video delivery over fixed and mobile networks, and even beyond allowing communication and collaboration among things. It is expected to enable haptic communications and allow skill set delivery over networks. Some examples of potential applications are tele-surgery, vehicle fleets, augmented reality and industrial process automation. Several papers already cover many of the Tactile Internet-related concepts and technologies, such as haptic codecs, applications, and supporting technologies. However, none of them offers a comprehensive survey of the Tactile Internet, including its architectures and algorithms. Furthermore, none of them provides a systematic and critical review of the existing solutions. To address these lacunae, we provide a comprehensive survey of the architectures and algorithms proposed to date for the Tactile Internet. In addition, we critically review them using a well-defined set of requirements and discuss some of the lessons learned as well as the most promising research directions.
Cosmological Insights into the Early Accretion of r-process-enhanced Stars. I. A Comprehensive Chemodynamical Analysis of LAMOST J1109+0754

This study presents a comprehensive chemodynamical analysis of LAMOST J1109+0754, a bright (V = 12.8), extremely metal-poor ([Fe/H] = −3.17) star, with a strong r-process enhancement ([Eu/Fe] = +0.94 ± 0.12). Our results are based on the 7D measurements supplied by Gaia and the chemical composition derived from a high-resolution (R ∼ 110,000), high signal-to-noise ratio ( optical spectrum obtained by the 2.4 m Automated Planet Finder Telescope at Lick Observatory. We obtain chemical abundances of 31 elements (from lithium to thorium). The abundance ratios ([X/Fe]) of the light elements (Z ≤ 30) suggest a massive Population III progenitor in the 13.4–29.5 M⊙ mass range. The heavy-element (30 < Z ≤ 90) abundance pattern of J1109+075 agrees extremely well with the scaled-solar r-process signature. We have developed a novel approach to trace the kinematic history and orbital evolution of J1109+0754 with a cOsmologically deRIved timE-varyiNg Galactic poTential (the ORIENT) constructed from snapshots of a simulated Milky Way analog taken from the Illustris-TNG simulation. The orbital evolution within this Milky Way–like galaxy, along with the chemical abundance pattern, implies that J1109+0754 likely originated in a low-mass dwarf galaxy located ∼60 kpc from the center of the Galaxy, which was accreted ∼6–7 Gyr ago, and that the star now belongs to the outer-halo population.
Weird AI Yankovic: Generating Parody Lyrics

Lyrics parody swaps one set of words that accompany a melody with a new set of words, preserving the number of syllables per line and the rhyme scheme. Lyrics parody generation is a challenge for controllable text generation. We show how a specialized sampling procedure, combined with backward text generation with XLNet can produce parody lyrics that reliably meet the syllable and rhyme scheme constraints.We introduce the Weird AI Yankovic system and provide a case study evaluation. We conclude with societal implications of neural lyric parody generation.
Microtubule-based actin transport and localization in a spherical cell

The interaction between actin filaments and microtubules is crucial for many eukaryotic cellular processes, such as, among others, cell polarization, cell motility and cellular wound healing. The importance of this interaction has long been recognized, yet very little is understood about both the underlying mechanisms and the consequences for the spatial (re)organization of the cellular cytoskeleton. At the same time, understanding the causes and the consequences of the interaction between different biomolecular components are key questions for in vitro research involving reconstituted biomolecular systems, especially in the light of current interest in creating minimal synthetic cells. In this light, recent in vitro experiments have shown that the actin-microtubule interaction mediated by the cytolinker TipAct, which binds to actin lattice and microtubule tips, causes the directed transport of actin filaments. We develop an analytical theory of dynamically unstable microtubules, nucleated from the centre of a spherical cell, in interaction with actin filaments. We show that, depending on the balance between the diffusion of unbound actin filaments and propensity to bind microtubules, actin is either concentrated in the centre of the cell, where the density of microtubules is highest, or becomes localized to the cell cortex.
BIOMARKERS IN ONCOLOGY

A biomarker or biological marker is in general a substance used as an indicator of biological state. Biomarkers are characteristic biological properties that can be detected and measured in parts of the body like the blood or tissue. They may indicate either normal or diseased processes in the body. Biomarkers can be specific cells, molecules, or genes, gene products, enzymes, or hormones. As an important biological indicator of cancer status and progression for the physiological state of the cell at a specific time, biomarkers represent powerful tools for monitoring the course of cancer and gauging efficacy and safety of novel therapeutic agents. The recent progress of proteomics has opened new avenues for cancerrelated biomarker discovery. Advances in proteomics are contributing to the understanding of pathophysiology of neoplasia, cancer diagnosis, and anticancer drug discovery. One of the major contributions proteomics has made to the medical and pharmaceutical communities is the identification of potential drug targets.
Generate Novel Molecules With Target Properties Using Conditional Generative Models

Drug discovery using deep learning has attracted a lot of attention of late as it has obvious advantages like higher efficiency, less manual guessing and faster process time. In this paper, we present a novel neural network for generating small molecules similar to the ones in the training set. Our network consists of an encoder made up of bi-GRU layers for converting the input samples to a latent space, predictor for enhancing the capability of encoder made up of 1D-CNN layers and a decoder comprised of uni-GRU layers for reconstructing the samples from the latent space representation. Condition vector in latent space is used for generating molecules with the desired properties. We present the loss functions used for training our network, experimental details and property prediction metrics. Our network outperforms previous methods using Molecular weight, LogP and Quantitative Estimation of Drug-likeness as the evaluation metrics.