Miracle-dz/UniFAR-FTUs huggingface dataset:
A Visual Analytics Framework for Contrastive Network Analysis

A common network analysis task is comparison of two networks to identify unique characteristics in one network with respect to the other. For example, when comparing protein interaction networks derived from normal and cancer tissues, one essential task is to discover protein-protein interactions unique to cancer tissues. However, this task is challenging when the networks contain complex structural (and semantic) relations. To address this problem, we design ContraNA, a visual analytics framework leveraging both the power of machine learning for uncovering unique characteristics in networks and also the effectiveness of visualization for understanding such uniqueness. The basis of ContraNA is cNRL, which integrates two machine learning schemes, network representation learning (NRL) and contrastive learning (CL), to generate a low-dimensional embedding that reveals the uniqueness of one network when compared to another. ContraNA provides an interactive visualization interface to help analyze the uniqueness by relating embedding results and network structures as well as explaining the learned features by cNRL. We demonstrate the usefulness of ContraNA with two case studies using real-world datasets. We also evaluate ContraNA through a controlled user study with 12 participants on network comparison tasks. The results show that participants were able to both effectively identify unique characteristics from complex networks and interpret the results obtained from cNRL.
First record of the Indo-Pacific species Electroma vexillum (Mollusca: Bivalvia: Pterioida) in the eastern Mediterranean

Iskenderun Bay (north-west Levantine Sea) is intensely industrialized but it is oil pollution that affects most of the coastal and marine ecosystems in the area. Molluscan species diversity in the bay is changing rapidly over the last decades due to the introduction of non-indigenous species of Indo-Pacific origin. To date, 67 of the 181 recorded molluscan species are aliens (37%), a percentage by far higher than anywhere reported. A population of the bivalve Electroma vexillum , whose original distribution is the Indian Ocean and the Red Sea, was encountered and recorded for the first time in 2002 in the discharge canals of the Iskenderun Iron and Steel Factory. The distribution of E. vexillum was very restricted in the canal, presumably limited by the temperature (23–36°C). Its occurrence is attributed to shipping.
Investigating the association between meteorological factors and the transmission and fatality of COVID-19 in the US

A novel coronavirus disease (COVID-19) is sweeping the world and has taken away thousands of lives. As the current epicenter, the United States has the largest number of confirmed and death cases of COVID-19. Meteorological factors have been found associated with many respiratory diseases in the past studies. In order to understand that how and during which period of time do the meteorological factors have the strongest association with the transmission and fatality of COVID-19, we analyze the correlation between each meteorological factor during different time periods within the incubation window and the confirmation and fatality rate, and develop statistic models to quantify the effects at county level. Results show that meteorological variables except maximum wind speed during the day 13 - 0 before current day shows the most significant correlation (P < 0.05) with the daily confirmed rate, while temperature during the day 13 - 8 before are most significantly correlated (P < 0.05) with the daily fatality rate. Temperature is the only meteorological factor showing dramatic positive association nationally, particularly in the southeast US where the current outbreak most intensive. The influence of temperature is remarkable on the confirmed rate with an increase of over 5 pmp in many counties, but not as much on the fatality rate (mostly within 0.01%). Findings in this study will help understanding the role of meteorological factors in the spreading of COVID-19 and provide insights for public and individual in fighting against this global epidemic.
An integrated risk assessment for climate change: analysing the vulnerability of sharks and rays on Australia's Great Barrier Reef

An Integrated Risk Assessment for Climate Change (IRACC) is developed and applied to assess the vulnerability of sharks and rays on Australia's Great Barrier Reef (GBR) to climate change. The IRACC merges a traditional climate change vulnerability framework with approaches from fisheries ecological risk assessments. This semi‐quantitative assessment accommodates uncertainty and can be applied at different spatial and temporal scales to identify exposure factors, at‐risk species and their key biological and ecological attributes, critical habitats a`nd ecological processes, and major knowledge gaps. Consequently, the IRACC can provide a foundation upon which to develop climate change response strategies. Here, we describe the assessment process, demonstrate its application to GBR shark and ray species, and explore the issues affecting their vulnerability to climate change. The assessment indicates that for the GBR, freshwater/estuarine and reef associated sharks and rays are most vulnerable to climate change, and that vulnerability is driven by case‐specific interactions of multiple factors and species attributes. Changes in temperature, freshwater input and ocean circulation will have the most widespread effects on these species. Although relatively few GBR sharks and rays were assessed as highly vulnerable, their vulnerability increases when synergies with other factors are considered. This is especially true for freshwater/estuarine and coastal/inshore sharks and rays. Reducing the impacts of climate change on the GBR's sharks and rays requires a range of approaches including mitigating climate change and addressing habitat degradation and sustainability issues. Species‐specific conservation actions may be required for higher risk species (e.g. the freshwater whipray, porcupine ray, speartooth shark and sawfishes) including reducing mortality, preserving coastal catchments and estuarine habitats, and addressing fisheries sustainability. The assessment identified many knowledge gaps concerning GBR habitats and processes, and highlights the need for improved understanding of the biology and ecology of the sharks and rays of the GBR.
Interpretable Sequence Learning for COVID-19 Forecasting

We propose a novel approach that integrates machine learning into compartmental disease modeling to predict the progression of COVID-19. Our model is explainable by design as it explicitly shows how different compartments evolve and it uses interpretable encoders to incorporate covariates and improve performance. Explainability is valuable to ensure that the model's forecasts are credible to epidemiologists and to instill confidence in end-users such as policy makers and healthcare institutions. Our model can be applied at different geographic resolutions, and here we demonstrate it for states and counties in the United States. We show that our model provides more accurate forecasts, in metrics averaged across the entire US, than state-of-the-art alternatives, and that it provides qualitatively meaningful explanatory insights. Lastly, we analyze the performance of our model for different subgroups based on the subgroup distributions within the counties.
Histopathology of Fusarium wilt of staghorn sumac (Rhus typhina) caused by Fusarium oxysporum f. sp. callistephi race 3. III. Host cell and tissue reactions

Various cell reactions occurred in staghorn sumac plants inoculated with Fusarium oxysporum f. sp. callistephi. Light and transmission electron microscopy observations and results of cytochemical tests showed: 1) increased laticifers and latex production in the phloem; 2) tylosis formation; 3) host cell wall modifications, including appositions or other cell wall thickenings; and 4) unusual cross wall formation in some cells, and cell hypertrophy and hyperplasia. Tylosis walls labelled for pectin and cellulose and many displayed inner suberin-like layers. These layers were also noted in cells of the medullary sheath and in many cells with dense content and thickened walls in the barrier zones that had formed. These zones also contained fibres with newly-formed gelatinous-like layers. In the vicinity of these cells, host cell walls were frequently altered, associated with opaque matter. Many small particles present in chains also occurred in some of these cells, which contained only remnants of host cytoplasm. Light microscopy observations showed that pronounced tissue proliferation and aberrant cells occurred in the outer xylem in the infected plants. Unusual neoplasmic tissue also formed from cells surrounding the pith and medullary sheath, and it spanned directly across the pre-existing xylem tissue and burst as large mounds on the stems.
Image-guided Fine Needle Aspiration Cytology of Intrathoracic Lesions

Background: Percutaneous, image-guided transthoracic fine needle aspiration cytology (TTFNAC) is a rapid, yet accurate, and well-established diagnostic method used in the cytological evaluation of intrathoracic lesions. The study was done to determine the utility of image-guided TTFNAC in diagnosis of intrathoracic lesions. Subjects and Methods: A retrospective analysis of all cases who underwent image-guided TTFNAC of a suspected intrathoracic lesion, in a tertiary care hospital was done over a period of 3 years. Results: During the study period, 124 cases of image-guided FNAC of intrathoracic lesions were obtained. The mean age at presentation was 60.5 years with M:F: 3.6:1. Neoplastic lesions (71.5%) outnumbered the nonneoplastic lesions (28.5%). The most common tumor was adenocarcinoma (25%) followed by squamous cell carcinoma (SCC, 11%), and small cell carcinoma (5%). There was one case each of anaplastic carcinoma, plasmacytoma, bronchoalveolar carcinoma, and non-Hodgkin lymphoma (NHL). Most of the lesions were found on the right side and upper lobe. Among the mediastinal lesions, we found two cases of thymoma and one case each of NHL)/primitive neuroectodermal tumor (PNET), NHL, and small cell carcinoma metastasis to lymph node followed by ten cases of inflammatory lesions and seven cases of tuberculosis (TB). Conclusion: Image-guided TTFNAC of intrathoracic lesions is a safe method when done by well-trained medical personnel with lesser rate of complications. An early accurate diagnosis of malignancy can be made based on the cytological features; however, further subtyping of the malignancy may sometimes be difficult due to overlapping cytological features. TTFNAC can be a diagnostic tool for identifying nonneoplastic lesion such as TB. Hence, image-guided FNAC aids in early diagnosis and management of patients with intrathoracic lesions.
The partition complex: an invitation to combinatorial commutative algebra

We provide a new foundation for combinatorial commutative algebra and Stanley-Reisner theory using the partition complex introduced in [Adi18]. One of the main advantages is that it is entirely self-contained, using only a minimal knowledge of algebra and topology. On the other hand, we also develop new techniques and results using this approach. In particular, we provide - A novel, self-contained method of establishing Reisner's theorem and Schenzel's formula for Buchsbaum complexes. - A simple new way to establish Poincare duality for face rings of manifolds, in much greater generality and precision than previous treatments. - A "master-theorem" to generalize several previous results concerning the Lefschetz theorem on subdivisions. - Proof for a conjecture of Kuhnel concerning triangulated manifolds with boundary.
Double-heavy tetraquark states with heavy diquark-antiquark symmetry *

We make a calculation on the masses of the $QQ\bar{q}\bar{q}$ ($Q=c,b$; $q=u,d,s$) tetraquark states with the help of heavy diquark-antiquark symmetry (HDAS) and the chromomagnetic interaction (CMI) model. The masses of highest-spin ($J=2$) tetraquarks having only the $(QQ)_{\bar{3}_c}(\bar{q}\bar{q})_{3_c}$ color structure are related with those of conventional hadrons by using HDAS. Then the masses of their partner states are determined with the mass splittings in the CMI model. From our numerical results, we find that: (i) the lightest $cc\bar{n}\bar{n}$ ($n=u,d$) is an $I(J^P)=0(1^+)$ state around 3929 MeV (53 MeV above the $DD^*$ threshold) and all the double-charm tetraquarks are not stable; (ii) stable double-bottom tetraquarks are the lowest $0(1^+)$ $bb\bar{n}\bar{n}$ around 10488 MeV ($\approx116$ MeV below the $BB^*$ threshold) and the lowest $1/2(1^+)$ $bb\bar{n}\bar{s}$ around 10671 MeV ($\approx20$ MeV below the $BB_s^*/B_sB^*$ threshold); and (iii) the two lowest $bc\bar{n}\bar{n}$ tetraquarks, the lowest $0(0^+)$ around 7167 MeV and the lowest $0(1^+)$ around 7223 MeV, are near-threshold states. We also discuss the constraints on masses of the double-heavy hadrons. Specifically, for the lowest nonstrange tetraquarks, one has $T_{cc}<3965$ MeV, $T_{bb}<10627$ MeV, and $T_{bc}<7199$ MeV.
Geometric structures, the Gromov order, Kodaira dimensions and simplicial volume

We introduce an axiomatic definition for the Kodaira dimension and classify Thurston geometries in dimensions $\leq 5$ in terms of this Kodaira dimension. We show that the Kodaira dimension is monotone with respect to the partial order defined by maps of non-zero degree between 5-manifolds. We study the compatibility of our definition with traditional notions of Kodaira dimension, especially the highest possible Kodaira dimension. To this end, we establish a connection between the simplicial volume and the holomorphic Kodaira dimension, which in particular implies that any smooth Kahler 3-fold with non-vanishing simplicial volume has top holomorphic Kodaira dimension.
Regularization by Denoising via Fixed-Point Projection (RED-PRO)

Inverse problems in image processing are typically cast as optimization tasks, consisting of data fidelity and stabilizing regularization terms. A recent regularization strategy of great interest utilizes the power of denoising engines. Two such methods are the Plug-and-Play Prior (PnP) and Regularization by Denoising (RED). While both have shown state-of-the-art results in various recovery tasks, their theoretical justification is incomplete. In this paper, we aim to enrich the understanding of RED and its connection to PnP. Towards that end, we reformulate RED as a convex optimization problem utilizing a projection (RED- PRO) onto the fixed-point set of demicontractive denoisers. We offer a simple iterative solution to this problem, and establish a novel unification of RED-PRO and PnP, while providing guarantees for their convergence to the globally optimal solution. We also present several relaxations of RED-PRO that allow for handling denoisers with limited fixed-point sets. Finally, we demonstrate RED-PRO for the tasks of image deblurring and super-resolution, showing improved results with respect to the original RED framework.
Small-scale tests of inflation

We investigate small-scale signatures of the inflationary particle content. We consider the case of a light spin-2 particle sourcing primordial gravitational waves by employing an effective field theory description. Upon allowing time-dependent sound speeds for the helicity modes, this setup delivers a blue tensor spectrum detectable, for example, by upcoming laser interferometers. Our focus is on the tensor non-Gaussianities that ensue from this field configuration. After characterising the bispectrum amplitude and shape-function at CMB scales, we move on to smaller scales where anisotropies induced in the tensor power spectrum by long-short modes coupling become the key handle on (squeezed) primordial non-Gaussianities. We identify the parameter space generating percent level anisotropies at scales soon to be probed by SKA and LISA.
Composição, abundância e distribuição espacial do zooplâncton no complexo estuarino de Paranaguá durante o inverno de 1993 e o verão de 1994

A estrutura das associacoes zooplanctonicas do complexo estuarino de Paranagua foi estudada no inverno de 1993 e no verao de 1994. Copepodes dos generos Acartia, Paracalanus, Parvocalanus, Temora, Pseudodiaptomus, Oithona e Euterpina foram dominantes, atingindo ate cerca de 90% da densidade total. A unica especie que ocorreu preferencialmente em salinidades inferiores a 15 foi o calanoide Pseudodiaptomus richardi, que representa o principal componente do holoplncton nos setores oligohalinos. Acartia tonsa e Oithona oswaldocruzi predominaram nos trechos intermediarios, associadas a outras especies estuarino-marinhas que suportam maiores variacoes de salinidade, como Acartia lilljeborgi, Pseudodiaptomus acutus e Oithona hebes. Especies marinho-eurihalinas como Temora turbinata, Paraca/anus quasimodo, Oithona simplex e Euterpina acutifrons ocorreram em salinidades tao baixas quanto 15, mas foram mais abundantes na area externa influenciada pela agua costeira. Varias especies marinho-estenohalinas, associadas principalmente as aguas quentes da Corrente do Brasil, foram registradas no setor euhalino. Outros grupos zooplanctonicos numericamente importantes foram os tintinineos, apendicularias, cladoceros e alguns representantes do meroplncton, como as larvas de poliquetas e decapodes. Os maximos de abundncia do zooplncton (ate cerca de 82000 org.m-3) ocorreram nos setores intermediarios, em salinidades variando entre 15 e 30, coincidindo aproximadamente com o padrao de distribuicao da biomassa fitoplanctonica.
Fate of Duplicated Neural Structures

Statistical physics determines the abundance of different arrangements of matter depending on cost-benefit balances. Its formalism and phenomenology percolate throughout biological processes and set limits to effective computation. Under specific conditions, self-replicating and computationally complex patterns become favored, yielding life, cognition, and Darwinian evolution. Neurons and neural circuits sit at a crossroads between statistical physics, computation, and (through their role in cognition) natural selection. Can we establish a statistical physics of neural circuits? Such theory would tell what kinds of brains to expect under set energetic, evolutionary, and computational conditions. With this big picture in mind, we focus on the fate of duplicated neural circuits. We look at examples from central nervous systems, with stress on computational thresholds that might prompt this redundancy. We also study a naive cost-benefit balance for duplicated circuits implementing complex phenotypes. From this, we derive phase diagrams and (phase-like) transitions between single and duplicated circuits, which constrain evolutionary paths to complex cognition. Back to the big picture, similar phase diagrams and transitions might constrain I/O and internal connectivity patterns of neural circuits at large. The formalism of statistical physics seems to be a natural framework for this worthy line of research.
Energy Transfer by Nonlinear Alfvén Waves in the Solar Chromosphere and Its Effect on Spicule Dynamics, Coronal Heating, and Solar Wind Acceleration

Alfvén waves are responsible for the transfer of magnetic energy in magnetized plasma. They are involved in heating the solar atmosphere and driving solar wind through various nonlinear processes. Because the magnetic field configurations directly affect the nonlinearity of Alfvén waves, it is important to investigate how they relate to the solar atmosphere and wind structure through the nonlinear propagation of Alfvén waves. In this study, we carried out one-dimensional magnetohydrodynamic simulations to realize the above relation. The results show that when the nonlinearity of Alfvén waves in the chromosphere exceeds a critical value, the dynamics of the solar chromosphere (e.g., spicule) and the mass-loss rate of solar wind tend to be independent of the energy input from the photosphere. In a situation where the Alfvén waves are highly nonlinear, the strong shear torsional flow generated in the chromosphere “fractures” the magnetic flux tube. This corresponds to the formation of chromospheric intermediate shocks, which limit the transmission of the Poynting flux into the corona by Alfvén waves and also inhibits the propagation of chromospheric slow shock.
Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset

From conspiracy theories to fake cures and fake treatments, COVID-19 has become a hot-bed for the spread of misinformation online. It is more important than ever to identify methods to debunk and correct false information online. In this paper, we present a methodology and analyses to characterize the two competing COVID-19 misinformation communities online: (i) misinformed users or users who are actively posting misinformation, and (ii) informed users or users who are actively spreading true information, or calling out misinformation. The goals of this study are two-fold: (i) collecting a diverse set of annotated COVID-19 Twitter dataset that can be used by the research community to conduct meaningful analysis; and (ii) characterizing the two target communities in terms of their network structure, linguistic patterns, and their membership in other communities. Our analyses show that COVID-19 misinformed communities are denser, and more organized than informed communities, with a possibility of a high volume of the misinformation being part of disinformation campaigns. Our analyses also suggest that a large majority of misinformed users may be anti-vaxxers. Finally, our sociolinguistic analyses suggest that COVID-19 informed users tend to use more narratives than misinformed users.
Using Actor-Based Theories Of Change to Conduct Robust Contribution Analysis in Complex Settings

The use of theories of change (ToCs) is a hallmark of sound evaluation practice. As interventions have become more complex, the development of ToCs that adequately unpack this complexity has become more challenging. Equally important is the development of evaluable ToCs, necessary for conducting robust theory-based evaluation approaches such as contribution analysis (CA). This article explores one approach to tackling these challenges through the use of nested actor-based ToCs using the case of an impact evaluation of a complex police-reform program in the Democratic Republic of Congo, describing how evaluable nested actor-based ToCs were built to structure the evaluation.
Compactified Spacelike Extra Dimension & Brane-Higgs Field

In the paradigm with a small warped Spacelike Extra Dimensions (SED), the Higgs field is in general localized at a boundary of the SED (TeV-brane) where the gravity scale is redshifted to the TeV by a warp factor. If the SM gauge bosons and fermions propagate into the warped SED, one can generate the mass hierarchy for fermions. It is thus crucial to treat carefully the TeV-brane localized masses for such fermions, which is done in the literature by applying a regularization process suffering from a lack of consistency and more importantly being useless, as we demonstrate in detail in the present thesis. The first part of the thesis is devoted to the treatment of brane localized mass terms for 5D fermions, which requires the introduction of new Lagrangian terms at the SED boundaries, similar to the Gibbons-Hawking terms in gravity. The second part consists in applying different methods (function/distribution fields, 4D/5D calculations, etc) to various brane localized terms (kinetic terms, Majorana masses, etc), as well as a generalization to several classified models (flat/warped dimensions, intervalle/orbifold, etc). In the third part, we propose to compactify a flat SED on a star/rose graph with a large number of identical small leaves/petals. We obtain a compactified space with a large volume without a large compactification length to stabilize. We use the approach of 5D fermions to build a toy model of small Dirac neutrino masses (brane localized left-handed neutrinos and bulk right-handed ones).