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Predicting and Detecting Emerging Cyberattack Patterns Using StreamWorks
The number and sophistication of cyberattacks on industries and governments have dramatically grown in recent years. To counter this movement, new advanced tools and techniques are needed to detect cyberattacks in their early stages such that defensive actions may be taken to avert or mitigate potential damage. From a cybersecurity analysis perspective, detecting cyberattacks may be cast as a problem of identifying patterns in computer network traffic. Logically and intuitively, these patterns may take on the form of a directed graph that conveys how an attack or intrusion propagates through the computers of a network. Such cyberattack graphs could provide cybersecurity analysts with powerful conceptual representations that are natural to express and analyze. We have been researching and developing graph-centric approaches and algorithms for dynamic cyberattack detection. The advanced dynamic graph algorithms we are developing will be packaged into a streaming network analysis framework known as StreamWorks. With StreamWorks, a scientist or analyst may detect and identify precursor events and patterns as they emerge in complex networks. This analysis framework is intended to be used in a dynamic environment where network data is streamed in and is appended to a large-scale dynamic graph. Specific graphical query patterns are decomposed and collected into a graph query library. The individual decomposed subpatterns in the library are continuously and efficiently matched against the dynamic graph as it evolves to identify and detect early, partial subgraph patterns. The scalable emerging subgraph pattern algorithms will match on both structural and semantic network properties.
Equilibrium-gated pattern formation: How molecular dissociation thermodynamics drive emergent behavior in dissipative polymeric systems
Emergent patterns in biological systems arise through dissipative processes that balance reaction and transport phenomena, producing highly functional properties from self-regulating mechanisms. Synthetic fabrication, by contrast, often relies on user-controlled, multistep methods that lack the self-organizing capabilities of natural systems. Inspired by nature, we sought chemical systems that integrate strongly coupled reaction and transport phenomena, identifying frontal ring-opening metathesis polymerization (FROMP) as a method capable of creating diverse forms and functions through reactive processing. By employing discrete molecular initiators, FROMP allows precise control of key reaction steps—inhibition, initiation, and propagation. Using an integrated computational and experimental framework, we uncover how near-equilibrium inhibition dynamics, coupled with far-from-equilibrium reaction kinetics, drive pattern formation in frontally polymerized synthetic materials. We propose the concept of equilibrium-gated pattern formation, demonstrating how initiator chemistry can be tuned to achieve programmable macroscale properties. Our study reveals a surprising insight: Emergent behavior in FROMP systems arises from the inhibition-dominated regime of resin composition, expanding prior observations that such behavior is confined to a narrow compositional space near the boundary between front quenching and uniform front propagation. We identify a broader compositional window, far from the quenching regime, where emergent behavior reliably manifests. This expanded design space significantly enhances the operational flexibility of reactive systems and their capacity for self-organization. Furthermore, these insights provide a roadmap for designing bioinspired materials with self-organizing capabilities, unlocking possibilities in synthetic manufacturing.
Understanding and Leveraging the I/O Patterns of Emerging Machine Learning Analytics
The scientific community is currently experiencing unprecedented amounts of data generated by cutting-edge science facilities. Soon facilities will be producing up to 1 PB/s which will force scientist to use more autonomous techniques to learn from the data. The adoption of machine learning methods, like deep learning techniques, in large-scale workflows comes with a shift in the workflow’s computational and I/O patterns. These changes often include iterative processes and model architecture searches, in which datasets are analyzed multiple times in different formats with different model configurations in order to find accurate, reliable and efficient learning models. This shift in behavior brings changes in I/O patterns at the application level as well at the system level. These changes also bring new challenges for the HPC I/O teams, since these patterns contain more complex I/O workloads. In this paper we discuss the I/O patterns experienced by emerging analytical codes that rely on machine learning algorithms and highlight the challenges in designing efficient I/O transfers for such workflows. We comment on how to leverage the data access patterns in order to fetch in a more efficient way the required input data in the format and order given by the needs of the application and how to optimize the data path between collaborative processes. We will motivate our work and show performance gains with a study case of medical applications.
Assessing complexity and dynamics in epidemics: geographical barriers and facilitators of foot-and-mouth disease dissemination
Introduction: Physical and non-physical processes that occur in nature may influence biological processes, such as dissemination of infectious diseases. However, such processes may be hard to detect when they are complex systems. Because complexity is a dynamic and non-linear interaction among numerous elements and structural levels in which specific effects are not necessarily linked to any one specific element, cause-effect connections are rarely or poorly observed. Methods: To test this hypothesis, the complex and dynamic properties of geo-biological data were explored with high-resolution epidemiological data collected in the 2001 Uruguayan foot-and-mouth disease (FMD) epizootic that mainly affected cattle. County-level data on cases, farm density, road density, river density, and the ratio of road (or river) length/county perimeter were analyzed with an open-ended procedure that identified geographical clustering in the first 11 epidemic weeks. Two questions were asked: (i) do geo-referenced epidemiologic data display complex properties? and (ii) can such properties facilitate or prevent disease dissemination? Results: Emergent patterns were detected when complex data structures were analyzed, which were not observed when variables were assessed individually. Complex properties–including data circularity–were demonstrated. The emergent patterns helped identify 11 counties as ‘disseminators’ or ‘facilitators’ (F) and 264 counties as ‘barriers’ (B) of epidemic spread. In the early epidemic phase, F and B counties differed in terms of road density and FMD case density. Focusing on non-biological, geographical data, a second analysis indicated that complex relationships may identify B-like counties even before epidemics occur. Discussion: Geographical barriers and/or promoters of disease dispersal may precede the introduction of emerging pathogens. If corroborated, the analysis of geo-referenced complexity may support anticipatory epidemiological policies.
Ferromagnetic Resonance Modes in the Exchange-Dominated Limit in Cylinders of Finite Length
We analyze the magnetic mode structure of axially magnetized finite-length nanoscopic cylinders in a regime where the exchange interaction dominates, along with simulations of the mode frequencies of the ferrimagnet yttrium iron garnet. For the bulk modes, we find that the frequencies can be represented by an expression given by Herring and Kittel by using wavevector components obtained by fitting the mode patterns emerging from these simulations. In addition to the axial, radial, and azimuthal modes that are present in an infinite cylinder, we find localized “cap modes” that are “trapped” at the top and bottom cylinder faces by the inhomogeneous dipole field emerging from the ends. Semiquantitative explanations are given for some of the modes, in terms of a one-dimensional Schrodinger equation, which is valid in the exchange-dominant case. The assignment of the azimuthal-mode number is carefully discussed, and the frequency splitting of a few pairs of nearly degenerate modes is determined through the beat pattern emerging from them.
Multiband Observations of the Quasar PKS 2326–502 during Active and Quiescent Gamma-Ray States in 2010–2012
Quasi-simultaneous observations of the Flat Spectrum Radio Quasar PKS 2326−502 were carried out in the γ -ray, X-ray, UV, optical, near-infrared, and radio bands. Using these observations, we are able to characterize the spectral energy distribution (SED) of the source during two flaring and one quiescent γ -ray states. These data were used to constrain one-zone leptonic models of the SEDs of each flare and investigate the physical conditions giving rise to them. While modeling one flare required only changes in the electron spectrum compared to the quiescent state, modeling the other flare required changes in both the electron spectrum and the size of the emitting region. These results are consistent with an emerging pattern of two broad classes of flaring states seen in blazars. Type 1 flares are explained by changes solely in the electron distribution, whereas type 2 flares require a change in an additional parameter. This suggests that different flares, even in the same source, may result from different physical conditions or different regions in the jet.
Volatile monoterpene ‘fingerprints’ of resinous Protium tree species in the Amazon rainforest
Volatile terpenoid resins represent a diverse group of plant defense chemicals involved in defense against herbivory, abiotic stress, and communication. However, their composition in tropical forests remains poorly characterized. As a part of tree identification, the 'smell' of damaged trunks is widely used, but is highly subjective. Here, we analyzed trunk volatile monoterpene emissions from 15 species of the genus Protium in the central Amazon. By normalizing the abundances of 28 monoterpenes, 9 monoterpene 'fingerprint' patterns emerged, characterized by a distinct dominant monoterpene. While 4 of the 'fingerprint' patterns were composed of multiple species, 5 were composed of a single species. Moreover, among individuals of the same species, 6 species had a single 'fingerprint' pattern, while 9 species had two or more 'fingerprint' patterns among individuals. A comparison of 'fingerprints' between 2015 and 2017 from 15 individuals generally showed excellent agreement, demonstrating a strong dependence on species identity, but not time of collection. The results are consistent with a previous study that found multiple divergent copies of monoterpene synthase enzymes in Protium. We conclude that the monoterpene 'fingerprint' database has important implications for constraining Protium species identification and phylogenetic relationships and enhancing understanding of physiological and ecological functions of resins and their potential commercial applications.
The hysteresis response of soil CO 2 concentration and soil respiration to soil temperature
Diurnal hysteresis between soil temperature (T s ) and both CO 2 concentration ([CO 2 ]) and soil respiration rate (R s ) were reported across different field experiments. However, the causes of these hysteresis patterns remain a subject of debate, with biotic and abiotic factors both invoked as explanations. Here, to address these issues, a CO 2 gas transport model is developed by combining a layer-wise mass conservation equation for subsurface gas phase CO 2 , Fickian diffusion for gas transfer, and a CO 2 source term that depends on soil temperature, moisture, and photosynthetic rate. Using this model, a hierarchy of numerical experiments were employed to disentangle the causes of the hysteretic [CO 2 ]-T s and CO 2 flux T s (i.e., F-T s ) relations. Model results show that gas transport alone can introduce both [CO 2 ]-T s and F-T s hystereses and also confirm prior findings that heat flow in soils lead to [CO 2 ] and F being out of phase with T s , thereby providing another reason for the occurrence of both hystereses. The area (A hys ) of the [CO 2 ]-T s hysteresis near the surface increases, while the A hys of the Rs-Ts hysteresis decreases as soils become wetter. Moreover, a time-lagged carbon input from photosynthesis deformed the [CO 2 ]-T s and R s -T s patterns, causing a change in the loop direction from counterclockwise to clockwise with decreasing time lag. An asymmetric 8-shaped pattern emerged as the transition state between the two loop directions. Lastly, tracing the pattern and direction of the hysteretic [CO 2 ]-T s and R s -T s relations can provide new ways to fingerprint the effects of photosynthesis stimulation on soil microbial activity and detect time lags between rhizospheric respiration and photosynthesis.
RE-INTEGRATE EMT Simulation Software: Graph Convolutional Network for Sparse Matrix Pattern Detection
The increasing complexity of power networks, driven by proliferation of inverters, presents analytical challenges that simplified models often fail to capture, necessitating Electromagnetic Transient (EMT) simulations. EMT models are represented as discretized differential-algebraic equations (DAEs), forming a linear system Ax = b that is computationally intensive to solve. Due to inherent sparsity of adjacency matrix A, distinct patterns emerge that, when accurately identified, enable efficient solver selection to minimize computation time. However, identifying ideal pattern is complicated by numerous reordering algorithms and limited structural insights. To address this, we introduce a Graph Convolutional Network (GCN) model for classifying sparse matrix patterns common in power system analysis. The model, achieving 96% test accuracy, is validated using PV plant models of 125 MW capacities connected to New England 39-bus transmission system (TS), and further scaled to a 4,992-bus network with 384 PV plants, yielding 191, 616 × 191, 616 sized A matrix. For all cases, the GCN model accurately identifies the matrix’s intrinsic sparse pattern, demonstrating its potential to enhance solver performance in EMT analysis.
A Critical Review of Heliostat Design for Concentrating Solar Thermal Technologies
Heliostat designs have undergone a widespread and eclectic development process, with many unique designs demonstrated. However, recent developments do not show the overall cohort converging toward a globally accepted universal design. Here, this study characterizes heliostats by breaking down and evaluating design traits based on emergent patterns from a comprehensive compilation of known heliostat designs spanning several decades. Four main categories for evaluation emerged: heliostat base, heliostat primary axis, heliostat drive, and facet support. Each of these four categories is further defined by four subtypes so that all heliostats fall into a single subtype within each category. The classified heliostats are ranked, yielding several view slices into the heliostat compilation or a breakdown of heliostats by type. An analysis of the breakdown shows several trends: a scale-up of established designs, new approaches at small to medium scales, and a movement toward greater adoption of linear drives. These trends reflect the most meaningful contributing factors to a proposed trajectory for a new era of heliostat designs striving to meet widely considered cost targets of $\$$50/m 2 or $\$$75/m 2 .
DeepFreeze: Towards Scalable Asynchronous Checkpointing of Deep Learning Models
In the age of big data, deep learning has emerged as a powerful tool to extract insight and exploit its value, both in industry and scientific applications. One common pattern emerging in such applications is frequent checkpointing of the state of the learning model during training, needed in a variety of scenarios: analysis of intermediate states to explain features and correlations with training data, exploration strategies involving alternative models that share a common ancestor, knowledge transfer, resilience, etc. However, with increasing size of the learning models and popularity of distributed data-parallel training approaches, simple checkpointing techniques used so far face several limitations: low serialization performance, blocking I/O, stragglers due to the fact that only a single process is involved in checkpointing. This paper proposes a checkpointing technique specifically designed to address the aforementioned limitations, introducing efficient asynchronous techniques to hide the overhead of serialization and I/O, and distribute the load over all participating processes. Experiments with two deep learning applications (CANDLE and ResNet) on a pre-Exascale HPC platform (Theta) shows significant improvement over state-of-art, both in terms of checkpointing duration and runtime overhead.
Cost-Effectiveness Analysis of Intensity Modulated Radiation Therapy Versus 3-Dimensional Conformal Radiation Therapy for Preoperative Treatment of Extremity Soft Tissue Sarcomas
Purpose: Create a cost-effectiveness model comparing preoperative intensity modulated radiation therapy (IMRT) versus 3-dimensional conformal radiation therapy (3DCRT) for extremity soft tissue sarcomas. Methods and Materials: Input parameters included 5-year local recurrence rates, rates of acute wound adverse events, and chronic toxicities (edema, fracture, joint stiffness, and fibrosis). Health-state utilities were used to calculate quality-adjusted life years (QALYs). Overall treatment costs per QALY or incremental cost-effectiveness ratio (ICER) were calculated. Roll-back analysis was performed using average costs and utilities to determine the baseline preferred radiation technique. One-way, 2-way, and probabilistic sensitivity analyses (PSA) were performed for input parameters with the largest impact on the ICER. Results: Overall treatment costs were $17,515.58 for 3DCRT compared with $22,920.51 for IMRT. The effectiveness was higher for IMRT (3.68 QALYs) than for 3DCRT (3.35 QALYs). The baseline ICER for IMRT was $16,842.75/QALY, making it the preferable treatment. The ICER was most sensitive to the probability of local recurrence, upfront radiation costs, local recurrence costs, certain utilities (no toxicity/no recurrence, grade 1 toxicity/no local recurrence, grade 4 toxicity/no local recurrence), and life expectancy. Dominance patterns emerged when the cost of 3DCRT exceeded $15,532.05 (IMRT dominates) or the life expectancy was under 1.68 years (3DCRT dominates). Furthermore, preference patterns changed based on the rate of local recurrence (threshold: 13%). The PSA results demonstrated that IMRT was the preferred cost-effective technique for 64% of trials compared with 36% for 3DCRT. Conclusions: Based on our model, IMRT is the preferred technique by lowering rates of local recurrence, severe toxicities, and improving QALYs. From a third-party payer perspective, IMRT should be a supported approach for extremity soft tissue sarcomas.
A MATHEMATICAL MODEL FOR AN HOURGLASS MAGNETIC FIELD
Starting with a mathematical boundary value problem for the magnetic vector potential in an axisymmetric cylindrical coordinate system, we derive a general solution for any arbitrary current distribution using the method of Green's functions. We use this to derive an analytic form for an hourglass magnetic field pattern created by electrical currents that are concentrated near (but not confined within) the equatorial plane of a cylindrical coordinate system. Our solution is not characterized by a cusp at the equatorial plane, as in previous solutions based on a current sheet. The pattern we derive provides a very good fit to hourglass magnetic field patterns emerging from three-dimensional numerical simulations of core formation, and can in principle be used for source-fitting of observed magnetic hourglass patterns.
A historical and comparative review of 50 years of root data collection in Puerto Rico
Fine roots play an important role in plant nutrition, as well as in carbon, water, and nutrient cycling. Fine roots account for a third of terrestrial net primary production (NPP), and inclusion of their structure and function in global carbon models should improve predictions of ecosystem responses to climate change. However, studies focusing on underground plant components are much less frequent than those on aboveground structure. This is more marked in the tropics, where one-third of the planet's terrestrial NPP is produced. Some tropical forests have been more represented in the literature than others, as demonstrated in the collective studies in Puerto Rico. This Caribbean island's biodiversity, frequency of natural disturbances, ease of access to forests, and long-term plots have created an ideal place for the study of tropical ecological processes. This review of the literature emphasizes 50 years of root research and patterns revealed around Puerto Rico. The data in this review were compiled from scientific publications, conference reports, symposiums, and raw data shared by some researches. Emergent patterns include the shallow distribution of fine roots, the great variation in root biomass among different forest types, little variation in root phosphorus concentrations, the slow recovery of root biomass after Hurricane Hugo, and the fact that most data on roots come from the wet tropical Luquillo Experimental Forest, causing other habitat types to be underrepresented. This review also shows the gaps in knowledge about fine roots in the island's ecosystems, which should be used to promote and guide future studies. Abstract in Spanish is available with online material.
3D pattern formation of a protein–membrane suspension
Many essential cellular processes, including cell division and the establishment of cell polarity during embryogenesis, are regulated by pattern-forming proteins. These proteins often need to bind to a substrate, such as the cell membrane, onto which they interact and form two-dimensional (2D) patterns. It is unclear how the membrane’s continuity and dimensionality impact pattern formation. Here, we address this gap using the MinDE system, a prototypical example of pattern-forming membrane proteins. We show that when the lipid substrate is fragmented into submicrometer-sized diffusive liposomes, adenosine triphosphate-driven protein–protein interactions generate three-dimensional (3D) spatially extended patterns, despite the complete loss of membrane continuity. Remarkably, these 3D patterns emerge at scales four orders of magnitude larger than the individual liposomes. By systematically varying protein concentration, liposome size, and density, we observed and characterized a variety of 3D dynamical patterns not seen on continuous 2D membranes, including traveling waves, dynamical spirals, and a coexistence phase. Simulations and linear stability analysis of a coarse-grained model revealed that the physical properties of the dispersed membrane effectively rescale both the protein–membrane binding rates and diffusion, two key parameters governing pattern formation and wavelength selection. These findings highlight the robustness of Min’s pattern-forming ability, suggesting that protein–membrane suspensions could serve as an adaptable template for studying out-of-equilibrium self-organization in 3D, beyond in vivo contexts.
Parametric study of flow patterns behind the standing accretion shock wave for core-collapse supernovae
In this study, we conduct three-dimensional hydrodynamic simulations systematically to investigate the flow patterns behind the accretion shock waves that are commonly formed in the post-bounce phase of core-collapse supernovae. Adding small perturbations to spherically symmetric, steady, shocked accretion flows, we compute the subsequent evolutions to find what flow pattern emerges as a consequence of hydrodynamical instabilities such as convection and standing accretion shock instability for different neutrino luminosities and mass accretion rates. Depending on these two controlling parameters, various flow patterns are indeed realized. We classify them into three basic patterns and two intermediate ones; the former includes sloshing motion (SL), spiral motion (SP), and multiple buoyant bubble formation (BB); the latter consists of spiral motion with buoyant-bubble formation (SPB) and spiral motion with pulsationally changing rotational velocities (SPP). Although the post-shock flow is highly chaotic, there is a clear trend in the pattern realization. The sloshing and spiral motions tend to be dominant for high accretion rates and low neutrino luminosities, and multiple buoyant bubbles prevail for low accretion rates and high neutrino luminosities. It is interesting that the dominant pattern is not always identical between the semi-nonlinear and nonlinear phases near the critical luminosity; the intermediate cases are realized in the latter case. Running several simulations with different random perturbations, we confirm that the realization of flow pattern is robust in most cases.