cssi/SciSciGPT-SciSciNet huggingface dataset:
Matching markets and cultural selection

We study the implications of two different matching mechanisms: bargaining in match (BIM) and binding agreement in the matching market (BAMM) in the context of cultural selection. Under BIM, matching across cultures is not operated through a well-regulated market, where agents’ payoffs are not determined by the market but through negotiation with their matched partners. Under BAMM, matching across cultures is operated through a formal market in which agents make binding agreements. We show that BIM results in inefficiency through cultural selection and possibly leads to assimilation, while BAMM restores efficiency and ensures cultural heterogeneity. The findings highlight the importance of regulating the matching market for multi-cultural societies.
Dynamics of semi-superfluid fluxtubes in color-flavor locked quark matter

At very high densities, as for example in the core of a neutron star, matter may appear in the color-flavor locked (CFL) phase, which is a superfluid. This phase features topologically stable vortex solutions, which arise in a spinning superfluid as localized configurations carrying quanta of angular momentum. Despite the topological stability of these vortices they are not the lowest energy state of the system at neutron star densities and decay into triplets of semi-superfluid fluxtubes. In these proceedings we report on the progress of our numerical study in the Ginzburg-Landau approach, where we investigate lattices of semi-superfluid fluxtubes. The fluxtubes are obtained through controlled decay of global vortex configurations in the presence of a gauge field. Understanding the dynamics of semi-superfluid string configurations is important in the context of angular momentum transfer from a quark matter core of a neutron star beyond the core boundary, since not vortex-, but fluxtube pinning seems to be the relevant mechanism in this scenario.
On a transform for modeling skewness

In many applications, data exhibit skewness and in this paper we present a new family of density functions modeling skewness based on a transformation, analogous to those of location and scale. Here we note that location will always refer to mode. Hence, in order to model data to include shape, we need only to find a family of densities exhibiting a variety of shapes, since we can obtain the other three properties via the transformations. The chosen class of densities with the variety of shape is, we argue, the simplest available. Illustrations including regression and time series models are given.
The Earth Microwave Background (EMB), Atmospheric Scattering and the Generation of Isotropy

In this work, the presence of substantial microwave power in the atmosphere of the Earth is discussed. It is advanced that this atmospheric microwave power constitutes pools of scattered photons initially produced, at least in substantial part, by the 3 K microwave background. The existence of these microwave pools of photons can serve to explain how the Earth, as an anisotropic source, is able to produce an Earth Microwave Background (EMB) at 3 K which is isotropic.
Sec17/Sec18 can support membrane fusion without help from completion of SNARE zippering

Membrane fusion requires R-, Qa-, Qb-, and Qc-family SNAREs that zipper into RQaQbQc coiled coils, driven by the sequestration of apolar amino acids. Zippering has been thought to provide all the force driving fusion. Sec17/αSNAP can form an oligomeric assembly with SNAREs with the Sec17 C-terminus bound to Sec18/NSF, the central region bound to SNAREs, and a crucial apolar loop near the N-terminus poised to insert into membranes. We now report that Sec17 and Sec18 can drive robust fusion without requiring zippering completion. Zippering-driven fusion is blocked by deleting the C-terminal quarter of any Q-SNARE domain or by replacing the apolar amino acids of the Qa-SNARE that face the center of the 4-SNARE coiled coils with polar residues. These blocks, singly or combined, are bypassed by Sec17 and Sec18, and SNARE-dependent fusion is restored without help from completing zippering.
Cohomological supports over derived complete intersections and local rings

A theory of cohomological support for pairs of DG modules over a Koszul complex is investigated. These specialize to the support varieties of Avramov and Buchweitz defined over a complete intersection ring, as well as support varieties over an exterior algebra. The main objects of study are certain DG modules over a polynomial ring; these determine the aforementioned cohomological supports and are shown to encode (co)homological information about pairs of DG modules over a Koszul complex. The perspective in this article leads to new proofs of well-known results for pairs of complexes over a complete intersection. Furthermore, these cohomological supports are used to define a support theory for pairs of objects in the derived category of an arbitrary commutative noetherian local ring. Finally, we calculate several examples; one of which answers a question of D. Jorgensen in the negative.
Eigenvectors and controllability of non-Hermitian random matrices and directed graphs

We study the eigenvectors and eigenvalues of random matrices with iid entries. Let N be a random matrix with iid entries which have symmetric distribution. For each unit eigenvector v of N our main results provide a small ball probability bound for linear combinations of the coordinates of v. Our results generalize the works of Meehan and Nguyen [59] as well as Touri and the second author [67, 68, 69] for random symmetric matrices. Along the way, we provide an optimal estimate of the probability that an iid matrix has simple spectrum, improving a recent result of Ge [37]. Our techniques also allow us to establish analogous results for the adjacency matrix of a random directed graph, and as an application we establish controllability properties of network control systems on directed graphs.
Clinicopathologic correlates and natural history of atypical chronic myeloid leukemia.

Background There are limited data on the clonal mechanisms underlying leukemogenesis, prognostic factors, and optimal therapy for atypical chronic myeloid leukemia (aCML). Methods The authors evaluated the clinicopathologic features, outcomes, and responses to therapy of 65 patients with aCML. The median age was 67 years (range, 46-89 years). Results The most frequently mutated genes included ASXL1 (83%), SRSF2 (68%), and SETBP1 (58%). Mutations in SETBP1, SRSF2, TET2, and GATA2 appeared at variant allele frequencies (VAFs) greater than 40%, whereas other RAS pathway mutations were more likely to appear at low VAFs. The acquisition of new, previously undetectable mutations at transformation was observed in 63% of the evaluable patients, with the most common involving signaling pathway mutations. Hypomethylating agents (HMAs) were associated with the highest response rates but with a short duration of response (median, 2.7 months). Therapy with ruxolitinib was not associated with clinically significant responses as a single agent or in combination with an HMA. Allogeneic stem cell transplantation was the only therapy associated with improved outcomes (hazard ratio, 0.144; 95% CI, 0.035-0.593; P = .007). Age, platelet counts, bone marrow blast percentages, and serum lactate dehydrogenase (LDH) levels were independent predictors of survival and were integrated in a multivariable model that allowed the prediction of 1- and 3-year survival. Conclusions aCML is characterized by high frequencies of ASXL1, SRSF2, and SETBP1 mutations and is associated with a high risk of acute myeloid leukemia transformation. Response and survival outcomes with current therapies remain poor. The incorporation of age, platelet counts, bone marrow blast percentages, and LDH levels can allow survival prediction, and allogeneic stem cell transplantation should be considered for all eligible patients.
OnionDNS: a seizure-resistant top-level domain

The Domain Name System (DNS) provides the critical service of mapping canonical names to IP addresses. Recognizing this, a number of parties have increasingly attempted to perform “domain seizures” on targets by having them delisted from DNS. Such operations often occur without providing due process to the owners of these domains, a practice made potentially worse by recent legislative proposals. We address this problem by creating OnionDNS, an anonymous top-level domain and resolution service for the Internet. Our solution relies on the establishment of a hidden service running DNS within Tor and uses a variety of mechanisms to ensure a high-performance architecture with strong integrity guarantees for resolved records. We then present our anonymous domain registrar and detail the protocol for securely transferring the service to another party. Finally, we also conduct both performance and legal analyses to further demonstrate the robustness of this approach. In so doing, we show that the delisting of domains from DNS can be mitigated in an efficient and secure manner.
Restoring Eye Contact to the Virtual Classroom with Machine Learning.

Nonverbal communication, in particular eye contact, is a critical element of the music classroom, shown to keep students on task, coordinate musical flow, and communicate improvisational ideas. Unfortunately, this nonverbal aspect to performance and pedagogy is lost in the virtual classroom. In this paper, we propose a machine learning system which uses single instance, single camera image frames as input to estimate the gaze target of a user seated in front of their computer, augmenting the user's video feed with a display of the estimated gaze target and thereby restoring nonverbal communication of directed gaze. The proposed estimation system consists of modular machine learning blocks, leading to a target-oriented (rather than coordinate-oriented) gaze prediction. We instantiate one such example of the complete system to run a pilot study in a virtual music classroom over Zoom software. Inference time and accuracy meet benchmarks for videoconferencing applications, and quantitative and qualitative results of pilot experiments include improved success of cue interpretation and student-reported formation of collaborative, communicative relationships between conductor and musician.
Reducing quantum annealing biases for solving the graph partitioning problem

Quantum annealers offer an efficient way to compute high quality solutions of NP-hard problems when expressed in a QUBO (quadratic unconstrained binary optimization) or an Ising form. This is done by mapping a problem onto the physical qubits and couplers of the quantum chip, from which a solution is read after a process called quantum annealing. However, this process is subject to multiple sources of biases, including poor calibration, leakage between adjacent qubits, control biases, etc., which might negatively influence the quality of the annealing results. In this work, we aim at mitigating the effect of such biases for solving constrained optimization problems, by offering a two-step method, and apply it to Graph Partitioning. In the first step, we measure and reduce any biases that result from implementing the constraints of the problem. In the second, we add the objective function to the resulting bias-corrected implementation of the constraints, and send the problem to the quantum annealer. We apply this concept to Graph Partitioning, an important NP-hard problem, which asks to find a partition of the vertices of a graph that is balanced (the constraint) and minimizes the cut size (the objective). We first quantify the bias of the implementation of the constraint on the quantum annealer, that is, we require, in an unbiased implementation, that any two vertices have the same likelihood of being assigned to the same or to different parts of the partition. We then propose an iterative method to correct any such biases. We demonstrate that, after adding the objective, solving the resulting bias-corrected Ising problem on the quantum annealer results in a higher solution accuracy.
Jackson, the Bank War, and the Legacy of the Second Bank of the United States

President Jackson vetoed the bill to recharter the Second Bank of the United States on July 10, 1832. I describe events leading to the veto and through the bank's dissolution in 1836 using private correspondence and official government documents. These sources reveal a political process through which charges against the bank took hold, accomplices and backup plans were lined up, and the bank was ultimately destroyed with the assistance of chartered banks in New York City. Although the aggressive means by which the bank was dismantled led to a system-wide financial failure and recession in the short term, the long-run outcome was likely a wider diffusion of banking services and a more efficient allocation of capital. The Federal Reserve benefited from applying a more rigorous regulatory structure onto the grid that the populists, free bankers, and National Banking System established.
Network-Level Optimization for Unbalanced Power Distribution System: Approximation and Relaxation

The nonlinear programming (NLP) problem to solve distribution-level optimal power flow (D-OPF) poses convergence issues and does not scale well for unbalanced distribution systems. The existing scalable D-OPF algorithms either use approximations that are not valid for an unbalanced power distribution system, or apply relaxation techniques to the nonlinear power flow equations that do not guarantee a feasible power flow solution. In this paper, we propose scalable D-OPF algorithms that simultaneously achieve optimal and feasible solutions by solving multiple iterations of approximate, or relaxed, D-OPF subproblems of low complexity. The first algorithm is based on a successive linear approximation of the nonlinear power flow equations around the current operating point, where the D-OPF solution is obtained by solving multiple iterations of a linear programming (LP) problem. The second algorithm is based on the relaxation of the nonlinear power flow equations as conic constraints together with directional constraints, which achieves optimal and feasible solutions over multiple iterations of a second-order cone programming (SOCP) problem. It is demonstrated that the proposed algorithms are able to reach an optimal and feasible solution while significantly reducing the computation time as compared to an equivalent NLP D-OPF model for the same distribution system.
What Causes Faint Solar Coronal Jets from Emerging Flux Regions in Coronal Holes

Using EUV images and line-of-sight magnetograms from Solar Dynamics Observatory, we examine eight emerging bipolar magnetic regions (BMRs) in central-disk coronal holes for whether the emerging magnetic arch made any noticeable coronal jets directly, via reconnection with ambient open field as modeled by Yokoyama and Shibata (1995). During emergence, each BMR produced no obvious EUV coronal jet of normal brightness, but each produced one or more faint EUV coronal jets that are discernible in AIA 193 A images. The spires of these jets are much fainter and usually narrower than for typical EUV jets that have been observed to be produced by minifilament eruptions in quiet regions and coronal holes. For each of 26 faint jets from the eight emerging BMRs, we examine whether the faint spire was evidently made a la Yokoyama and Shibata (1995). We find: (1) 16 of these faint spires evidently originate from sites of converging opposite-polarity magnetic flux and show base brightenings like those in minifilament-eruption-driven coronal jets, (2) the 10 other faint spires maybe were made by a burst of the external-magnetic-arcade-building reconnection of the emerging magnetic arch with the ambient open field, reconnection directly driven by the arch's emergence, but (3) none were unambiguously made by such emergence-driven reconnection. Thus, for these eight emerging BMRs the observations indicate that emergence-driven external reconnection of the emerging magnetic arch with ambient open field at most produces a jet spire that is much fainter than in previously-reported, much more obvious coronal jets driven by minifilament eruptions.
Wearing A Mask: Compressed Representations of Variable-Length Sequences Using Recurrent Neural Tangent Kernels

High dimensionality poses many challenges to the use of data, from visualization and interpretation, to prediction and storage for historical preservation. Techniques abound to reduce the dimensionality of fixed-length sequences, yet these methods rarely generalize to variable-length sequences. To address this gap, we extend existing methods that rely on the use of kernels to variable-length sequences via use of the Recurrent Neural Tangent Kernel (RNTK). Since a deep neural network with ReLu activation is a Max-Affine Spline Operator (MASO), we dub our approach Max-Affine Spline Kernel (MASK). We demonstrate how MASK can be used to extend principal components analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) and apply these new algorithms to separate synthetic time series data sampled from second-order differential equations.
Limit varieties of J -trivial monoids

We show that limit varieties of monoids recently discovered by Gusev, Zhang and Luo and their subvarieties are generated by monoids of the form $$M_\tau (W)$$ for certain congruences $$\tau $$ on the free monoid. The construction $$M_\tau (W)$$ is a generalization of widely used Dilworth–Perkins construction. Using this construction, we find explicit generators for Gusev limit varieties and give a short reproof to the fact that Zhang–Luo limit variety is non-finitely based.
Systematic Evaluation of Privacy Risks of Machine Learning Models

Machine learning models are prone to memorizing sensitive data, making them vulnerable to membership inference attacks in which an adversary aims to guess if an input sample was used to train the model. In this paper, we show that prior work on membership inference attacks may severely underestimate the privacy risks by relying solely on training custom neural network classifiers to perform attacks and focusing only on the aggregate results over data samples, such as the attack accuracy. To overcome these limitations, we first propose to benchmark membership inference privacy risks by improving existing non-neural network based inference attacks and proposing a new inference attack method based on a modification of prediction entropy. We also propose benchmarks for defense mechanisms by accounting for adaptive adversaries with knowledge of the defense and also accounting for the trade-off between model accuracy and privacy risks. Using our benchmark attacks, we demonstrate that existing defense approaches are not as effective as previously reported. Next, we introduce a new approach for fine-grained privacy analysis by formulating and deriving a new metric called the privacy risk score. Our privacy risk score metric measures an individual sample's likelihood of being a training member, which allows an adversary to identify samples with high privacy risks and perform attacks with high confidence. We experimentally validate the effectiveness of the privacy risk score and demonstrate that the distribution of privacy risk score across individual samples is heterogeneous. Finally, we perform an in-depth investigation for understanding why certain samples have high privacy risks, including correlations with model sensitivity, generalization error, and feature embeddings. Our work emphasizes the importance of a systematic and rigorous evaluation of privacy risks of machine learning models.
Observational Evidence for a Thick Disk of Dark Molecular Gas in the Outer Galaxy

We present the serendipitous discovery of an extremely broad ($\Delta V_{LSR} \sim 150$ km/s), faint ($T_{mb} 8 kpc) with the Green Bank Telescope. Originally discovered in 2015, we describe the redundant experimental, observational, and data quality tests of this result over the last five years. The longitude-velocity distribution of the emission unambiguously suggests large-scale Galactic structure. We observe a smooth distribution of OH in radial velocity that is morphologically similar to the HI radial velocity distribution in the outer Galaxy, showing that molecular gas is significantly more extended in the outer Galaxy than previously expected. Our results imply the existence of a thick ($-200< z < 200$ pc) disk of diffuse ($n_{H_{2}}$ $\sim$ 5 $\times$ 10$^{-3}$ cm$^{-3}$) molecular gas in the Outer Galaxy previously undetected in all-sky CO surveys.
Hierarchical Agglomerative Graph Clustering in Nearly-Linear Time

We study the widely used hierarchical agglomerative clustering (HAC) algorithm on edge-weighted graphs. We define an algorithmic framework for hierarchical agglomerative graph clustering that provides the first efficient $\tilde{O}(m)$ time exact algorithms for classic linkage measures, such as complete- and WPGMA-linkage, as well as other measures. Furthermore, for average-linkage, arguably the most popular variant of HAC, we provide an algorithm that runs in $\tilde{O}(n\sqrt{m})$ time. For this variant, this is the first exact algorithm that runs in subquadratic time, as long as $m=n^{2-\epsilon}$ for some constant $\epsilon > 0$. We complement this result with a simple $\epsilon$-close approximation algorithm for average-linkage in our framework that runs in $\tilde{O}(m)$ time. As an application of our algorithms, we consider clustering points in a metric space by first using $k$-NN to generate a graph from the point set, and then running our algorithms on the resulting weighted graph. We validate the performance of our algorithms on publicly available datasets, and show that our approach can speed up clustering of point datasets by a factor of 20.7--76.5x.
Mandoline: Model Evaluation under Distribution Shift

Machine learning models are often deployed in different settings than they were trained and validated on, posing a challenge to practitioners who wish to predict how well the deployed model will perform on a target distribution. If an unlabeled sample from the target distribution is available, along with a labeled sample from a possibly different source distribution, standard approaches such as importance weighting can be applied to estimate performance on the target. However, importance weighting struggles when the source and target distributions have non-overlapping support or are high-dimensional. Taking inspiration from fields such as epidemiology and polling, we develop Mandoline, a new evaluation framework that mitigates these issues. Our key insight is that practitioners may have prior knowledge about the ways in which the distribution shifts, which we can use to better guide the importance weighting procedure. Specifically, users write simple "slicing functions" - noisy, potentially correlated binary functions intended to capture possible axes of distribution shift - to compute reweighted performance estimates. We further describe a density ratio estimation framework for the slices and show how its estimation error scales with slice quality and dataset size. Empirical validation on NLP and vision tasks shows that \name can estimate performance on the target distribution up to $3\times$ more accurately compared to standard baselines.
Super-resolution label-free volumetric vibrational imaging.

Innovations in high-resolution optical imaging have allowed visualization of nanoscale biological structures and connections. However, super-resolution fluorescence techniques, including both optics-oriented and sample-expansion based, are limited in quantification and throughput especially in tissues from photobleaching or quenching of the fluorophores, and low-efficiency or non-uniform delivery of the probes. Here, we report a general sample-expansion vibrational imaging strategy, termed VISTA, for scalable label-free high-resolution interrogations of protein-rich biological structures with resolution down to 78 nm. VISTA achieves decent three-dimensional image quality through optimal retention of endogenous proteins, isotropic sample expansion, and deprivation of scattering lipids. Free from probe-labeling associated issues, VISTA offers unbiased and high-throughput tissue investigations. With correlative VISTA and immunofluorescence, we further validated the imaging specificity of VISTA and trained an image-segmentation model for label-free multi-component and volumetric prediction of nucleus, blood vessels, neuronal cells and dendrites in complex mouse brain tissues. VISTA could hence open new avenues for versatile biomedical studies. Super-resolution microscopy is often limited by photobleaching or uneven distribution of fluorophores. The authors present a label-free superresolution method termed VISTA, combining sample-expansion and vibrational imaging, with resolution down to 78 nm in protein-rich biological structures in cells and tissues.
VPIC 2.0: Next Generation Particle-in-Cell Simulations

VPIC is a general purpose particle-in-cell simulation code for modeling plasma phenomena such as magnetic reconnection, fusion, solar weather, and laser-plasma interaction in three dimensions using large numbers of particles. VPIC's capacity in both fidelity and scale makes it particularly well-suited for plasma research on pre-exascale and exascale platforms. In this article, we demonstrate the unique challenges involved in preparing the VPIC code for operation at exascale, outlining important optimizations to make VPIC efficient on accelerators. Specifically, we show the work undertaken in adapting VPIC to exploit the portability-enabling framework Kokkos and highlight the enhancements to VPIC's modeling capabilities to achieve performance at exascale. We assess the achieved performance-portability trade-off through a suite of studies on nine different varieties of modern pre-exascale hardware. Our performance-portability study includes weak-scaling runs on three of the top ten TOP500 supercomputers, as well as a comparison of low-level system performance of hardware from four different vendors.
Dual Ethics in Romans 13

Dharmaram Journals, a group of scientific periodical publications, is an integral part of Dharmaram Vidya Kshetram, Pontifical Athenaeum of Theology, Philosophy and Canon Law. We publish five academic and research journals, namely, Journal of Dharma, Asian Horizons, Vinayasadhana, Iustitia and Herald of the East in the fields of religions and philosophies, theology, formative spirituality and counselling, canon law and Chavara studies, respectively. Through these scientific publications, DVK accomplishes its mission by bringing to the erudite public the highest quality research.
Deep Reinforcement Learning for Adaptive Network Slicing in 5G for Intelligent Vehicular Systems and Smart Cities

Intelligent vehicular systems and smart city applications are the fastest growing Internet of things (IoT) implementations at a compound annual growth rate of 30%. In view of the recent advances in IoT devices and the emerging new breed of IoT applications driven by artificial intelligence (AI), fog radio access network (F-RAN) has been recently introduced for the fifth generation (5G) wireless communications to overcome the latency limitations of cloud-RAN (C-RAN). We consider the network slicing problem of allocating the limited resources at the network edge (fog nodes) to vehicular and smart city users with heterogeneous latency and computing demands in dynamic environments. We develop a network slicing model based on a cluster of fog nodes (FNs) coordinated with an edge controller (EC) to efficiently utilize the limited resources at the network edge. For each service request in a cluster, the EC decides which FN to execute the task, i.e., locally serve the request at the edge, or to reject the task and refer it to the cloud. We formulate the problem as infinite-horizon Markov decision process (MDP) and propose a deep reinforcement learning (DRL) solution to adaptively learn the optimal slicing policy. The performance of the proposed DRL-based slicing method is evaluated by comparing it with other slicing approaches in dynamic environments and for different scenarios of design objectives. Comprehensive simulation results corroborate that the proposed DRL-based EC quickly learns the optimal policy through interaction with the environment, which enables adaptive and automated network slicing for efficient resource allocation in dynamic vehicular and smart city environments.
Automated tracking of S. pombe spindle elongation dynamics.

The mitotic spindle is a microtubule-based machine that pulls the two identical sets of chromosomes to opposite ends of the cell during cell division. The fission yeast Schizosaccharomyces pombe is an important model organism for studying mitosis due to its simple, stereotyped spindle structure and well-established genetic toolset. S. pombe spindle length is a useful metric for mitotic progression, but manually tracking spindle ends in each frame to measure spindle length over time is laborious and can limit experimental throughput. We have developed an ImageJ plugin that can automatically track S. pombe spindle length over time and replace manual or semi-automated tracking of spindle elongation dynamics. Using an algorithm that detects the principal axis of the spindle and then finds its ends, we reliably track the length of the spindle as the cell divides. The plugin integrates with existing ImageJ features, exports its data for further analysis outside of ImageJ and does not require any programming by the user. Thus, the plugin provides an accessible tool for quantification of S. pombe spindle length that will allow automatic analysis of large microscopy data sets and facilitate screening for effects of cell biological perturbations on mitotic progression.
How to Maneuver Around in Eccentricity Vector Space

The GRAIL mission to the Moon will be the first time that two separate robotic orbiters will be placed into formation in orbit around a body other than Earth. The need to design an efficient series of maneuvers to shape the orbits and phasing of the two orbiters after arrival presents a significant challenge to mission designers. This paper presents a simple geometric method for relating in-plane impulsive maneuvers to changes in the eccentricity vector, which determines the shape and orientation of an orbit in the orbit plane. Examples then show how such maneuvers can accommodate desired changes to other orbital elements such as period, incination, and longitude of the ascending node.
A Generalization of a Theorem of Rothschild and van Lint

A classical result of Rothschild and van Lint asserts that if every non-zero Fourier coefficient of a Boolean function f over \(\mathbb {F}_2^{n}\) has the same absolute value, namely \(|\hat{f}(\alpha )|=1/2^k\) for every \(\alpha \) in the Fourier support of f, then f must be the indicator function of some affine subspace of dimension \(n-k\). In this paper we slightly generalize their result. Our main result shows that, roughly speaking, Boolean functions whose Fourier coefficients take values in the set \(\{-2/2^k, -1/2^k, 0, 1/2^k, 2/2^k\}\) are indicator functions of two disjoint affine subspaces of dimension \(n-k\) or four disjoint affine subspace of dimension \(n-k-1\). Our main technical tools are results from additive combinatorics which offer tight bounds on the affine span size of a subset of \(\mathbb {F}_2^{n}\) when the doubling constant of the subset is small.
Measurement and analysis of thermal conductivity of ceramic particle beds for solar thermal energy storage

Abstract A systematic study was performed to measure the effective thermal conductivity of ceramic particle beds, a promising heat transfer and thermal energy storage medium for concentrating solar power (CSP). The thermal conductivity of the ceramic particle beds was measured using a transient hot-wire (THW) method within a temperature range of room temperature to 700 °C, the target operating temperature of the next-generation CSP systems. Two different types of ceramic particles were examined: (1) CARBOBEAD HSP 40/70 and (2) CARBOBEAD CP 40/100 (also known as ACCUCAST ID 50) with the average particle sizes of ~405 μm and ~275 μm, respectively, and thermal conductivities ranging from ~0.25 W m−1 K−1 to ~0.50 W m−1 K−1 from 20 °C to 700 °C in both air and N2 gas. The gaseous pressure dependence of the thermal conductivity of the ceramic particle beds was also studied in the N2 environment to differentiate the contributions from gas conduction, solid conduction, and radiation. Calculations using the Zehner, Bauer, and Schlunder (ZBS) model showed good agreements with the measurements. Based on the model, it is concluded that the effective thermal conductivity of the packed particle beds is dominated by the gas conduction while the solid conduction and radiation contributes to about 20% of the effective thermal conductivity at high temperature.
Adjoint-Based, Three-Dimensional Error Prediction and Grid Adaptation

Engineering computational fluid dynamics analysis and design applications often focus on output functions, such as lift or drag. Errors in these output functions are generally unknown, and conservatively accurate solutions may be computed. Computable error estimates can offer the possibility to minimize computational work for a prescribed error tolerance. Such an estimate can be computed by solution of the flow equations and the linear adjoint problem for the functional of interest. The computational mesh can be modified to minimize the uncertainty of a computed error estimate. This robust mesh-adaptation procedure automatically terminates when the simulation is within a user-specified error tolerance. This procedure for estimation and adaptation to error in a functional is demonstrated for three-dimensional Euler problems. An adaptive mesh procedure that links to a CAD surface representation is demonstrated for wing, wing-body, and extruded high lift airfoil configurations. The error estimation and adaptation procedure yielded corrected functions that are as accurate as functions calculated on uniformly refined grids with many more grid points
Effect of Density on Noise Radiation from Subsonic Inverted Velocity Profile Jets

In this study, experiments were performed to investigate the influence of jet density in the generation of noise from inverted velocity profile subsonic jets. Such jets consist of low-speed gas flow in the center and a higher-speed annular flow. A combination of helium, nitrogen, and argon gases at various flow velocities were expanded through center and through annular convergent nozzles to obtain the desired density effects. Shadowgraphs of the jet flow were obtained, and mean velocity profiles and radiated noise were measured. The results clearly show that a difference in density between the inner and outer flows is an important factor in the development of the jet flow and in the production of jet noise.
Analyzing the Effect of Persistent Asset Switches on a Class of Hybrid-Inspired Optimization Algorithms

Convex optimization challenges are currently pervasive in many science and engineering domains. In many applications of convex optimization, such as those involving multi-agent systems and resource allocation, the objective function can persistently switch during the execution of an optimization algorithm. Motivated by such applications, we analyze the effect of persistently switching objectives in continuous-time optimization algorithms. In particular, we take advantage of existing robust stability results for switched systems with distinct equilibria and extend these results to systems described by differential inclusions, making the results applicable to recent optimization algorithms that employ differential inclusions for improving efficiency and/or robustness. Within the framework of hybrid systems theory, we provide an accurate characterization, in terms of Omega-limit sets, of the set to which the optimization dynamics converge. Finally, by considering the switching signal to be constrained in its average dwell time, we establish semi-global practical asymptotic stability of these sets with respect to the dwell-time parameter.
OPAR: Optimized Predictive and Adaptive Routing for Cooperative UAV Networks

Cooperative UAV networks are becoming increasingly popular in military and civilian applications. Alas, the typical ad-hoc routing protocols, which aim at finding the shortest path, lead to significant performance degradation because of the 3-dimension highly-dynamic nature of UAV networks and the uneven distribution of nodes across the network. This paper proposes OPAR, an optimized predictive and adaptive routing protocol, to face this challenging problem. We model the routing problem with linear programming (LP), where the goal is to maximize network performance, considering the path lifetime and path-length together. This model relies on a precise link lifetime prediction mechanism. We support the LP problem with a lightweight algorithm to find the optimized solution with a computation complexity of O(|E|2) , where |E| is the number of network links. We evaluate the OPAR performance and compare it with the well-known routing algorithms AODV, DSDV, and OLSR to cover a wide range of proactive and reactive protocols as well as distance vector and link-state techniques. We performed extensive simulations for different network densities and mobility patterns using the ns-3 simulator. Results show that OPAR prevents a high volume of routing traffic, increases the successful delivery by more than 30%, improves the throughput 25% on average, and decreases the flow completion time by an average of 35% 1.
A Machine Learning Approach to Classify Kinematics and Vortex Wake Modes of Oscillating Foils

Machine learning techniques have received attention in fluid dynamics in terms of predicting, clustering and classifying complex flow physics. One application has been the classification or clustering of various wake structures that emanate from bluff bodies such as cylinders or flapping foils, creating a rich diversity of vortex formations specific to flow conditions, geometry, and/or kinematics of the body. When utilizing oscillating foils to harvest energy from tidal or river flows, it is critical to understand the intricate and nonlinear relationship between flapping kinematics and the downstream vortex wake structure for optimal siting and operation of arrays. This paper develops a classification model to obtain groups of kinematics that contain similar wake patterns within the energy harvesting regime. Data is obtained through simulations of 27 unique oscillating foil kinematics for a total of 13,650 samples of the wake vorticity field. Within these samples three groups are visually labeled based on the relative angle of attack. A machine learning approach combining a convolutional neural network (CNN) with long short-term memory (LSTM) units is utilized to automatically classify the wakes into the three groups. The average accuracy on five test data subsets is 80% when the three visually labeled groups are used for classification. After analyzing the test subset with lowest accuracy, an update on the group division boundaries is proposed. With this update, the algorithm achieves an average accuracy of 90%, demonstrating that the three groups are able to discern distinct wake structures within a range of energy harvesting kinematics.
A framework to construct a longitudinal DW-MRI infant atlas based on mixed effects modeling of dODF coefficients.

Building of atlases plays a crucial role in the analysis of brain images. In scenarios where early growth, aging or disease trajectories are of key importance, longitudinal atlases become necessary as references, most often created from cross-sectional data. New opportunities will be offered by creating longitudinal brain atlases from longitudinal subject-specific image data, where explicit modeling of subject’s variability in slope and intercept leads to a more robust estimation of average trajectories but also to estimates of confidence bounds. This work focuses on a framework to build a continuous 4D atlas from longitudinal high angular resolution diffusion images (HARDI) where, unlike atlases of derived scalar diffusion indices such as FA, statistics on dODFs is preserved. Multi-scalar images obtained from DW images are used for geometric alignment, and linear mixed-effects modeling from longitudinal diffusion orientation distribution functions (dODF) leads to estimation of continuous dODF changes. The proposed method is applied to a longitudinal dataset of HARDI images from healthy developing infants in the age range of 3 to 36 months. Verification of mixed-effects modeling is obtained by voxel-wise goodness of fit calculations. To demonstrate the potential of our method, we display changes of longitudinal atlas using dODF and derived generalized fractional anisotropy (GFA) of dODF. We also investigate white matter maturation patterns in genu, body, and splenium of the corpus callosum. The framework can be used to build an average dODF atlas from HARDI data and to derive subject-specific and population-based longitudinal change trajectories.
Torsional strain engineering of transition metal dichalcogenide nanotubes: an ab initio study

We study the effect of torsional deformations on the electronic properties of single-walled transition metal dichalcogenide (TMD) nanotubes. In particular, considering forty-five select armchair and zigzag TMD nanotubes, we perform symmetry-adapted Kohn-Sham density functional theory calculations to determine the variation in bandgap and effective mass of charge carriers with twist. We find that metallic nanotubes remain so even after deformation, whereas semiconducting nanotubes experience a decrease in bandgap with twist-originally direct bandgaps become indirect-resulting in semiconductor to metal transitions. In addition, the effective mass of holes and electrons continuously decrease and increase with twist, respectively, resulting in n-type to p-type semiconductor transitions. We find that this behavior is likely due to rehybridization of orbitals in the metal and chalcogen atoms, rather than charge transfer between them. Overall, torsional deformations represent a powerful avenue to engineer the electronic properties of semiconducting TMD nanotubes, with applications to devices like sensors and semiconductor switches.
The rings of Saturn and Uranus

Saturn has bright, broad rings separated by narrow gaps, while the rings of Uranus are dark, narrow and widely spaced. Presumably, both sets of rings lie inside the Roche limit, which is why the ring material has not condensed into satellites. This paper briefly reviews what is known about each ring system, with emphasis on properties of significance to dynamical astronomy.
Electrical Propagation of Condensed and Diffuse Ions Along Actin Filaments

In this article, we elucidate the roles of divalent ion condensation and highly polarized immobile water molecules on the propagation of ionic calcium waves along actin filaments. We introduced a novel electrical triple layer model and used a non-linear Debye-Huckel theory with a non-linear, dissipative, electrical transmission line model to characterize the physicochemical properties of each monomer in the filament. This characterization is carried out in terms of an electric circuit model containing monomeric flow resistances and ionic capacitances in both the condensed and diffuse layers. We considered resting and excited states of a neuron using representative mono and divalent electrolyte mixtures. Additionally, we used 0.05V and 0.15V voltage inputs to study ionic waves along actin filaments in voltage clamp experiments. Our results reveal that the physicochemical properties characterizing the condensed and diffuse layers lead to different electrical conductive mediums depending on the ionic species and the neuron state. This region specific propagation mechanism provides a more realistic avenue of delivery by way of cytoskeleton filaments for larger charged cationic species. A new direct path for transporting divalent ions might be crucial for many electrical processes found in localized neuron elements such as at mitochondria and dendritic spines.
The degree of symmetry of lattice paths

The degree of symmetry of a combinatorial object, such as a lattice path, is a measure of how symmetric the object is. It typically ranges from zero, if the object is completely asymmetric, to its size, if it is completely symmetric. We study the behavior of this statistic on Dyck paths and grand Dyck paths, with symmetry described by reflection along a vertical line through their midpoint; partitions, with symmetry given by conjugation; and certain compositions interpreted as bargraphs. We find expressions for the generating functions for these objects with respect to their degree of symmetry, and their semilength or semiperimeter, deducing in most cases that, asymptotically, the degree of symmetry has a Rayleigh or half-normal limiting distribution. The resulting generating functions are often algebraic, with the notable exception of Dyck paths, for which we conjecture that it is D-finite (but not algebraic), based on a functional equation that we obtain using bijections to walks in the plane.
Prenatal Androgen Treatment Does Not Alter the Firing Activity of Hypothalamic Arcuate Kisspeptin Neurons in Female Mice.

Neuroendocrine control of reproduction is disrupted in many individuals with polycystic ovary syndrome, who present with increased luteinizing hormone (LH), and presumably gonadotropin-releasing hormone (GnRH), release frequency, and high androgen levels. Prenatal androgenization (PNA) recapitulates these phenotypes in primates and rodents. Female offspring of mice injected with dihydrotestosterone (DHT) on gestational D16-18 exhibit disrupted estrous cyclicity, increased LH and testosterone, and increased GnRH neuron firing rate as adults. PNA also alters the developmental trajectory of GnRH neuron firing rates, markedly blunting the prepubertal peak in firing that occurs in 3wk-old controls. GnRH neurons do not express detectable androgen receptors and are thus probably not the direct target of DHT. Rather, PNA likely alters GnRH neuronal activity by modulating upstream neurons, such as hypothalamic arcuate neurons co-expressing kisspeptin, neurokinin B (gene Tac2), and dynorphin, aka KNDy neurons. We hypothesized PNA treatment changes firing rates of KNDy neurons in a similar age-dependent manner as GnRH neurons. We conducted targeted extracellular recordings (0.5-2h) of Tac2-identified KNDy neurons from control and PNA mice at 3wks of age and in adulthood. About half of neurons were quiescent ( Significance statement Prenatal androgenization (PNA) recapitulates key aspects of the common reproductive disorder polycystic ovary syndrome. It is postulated that disruptions in the episodic pattern of gonadotropin-releasing hormone (GnRH) secretion in part underly this disorder, yet GnRH neurons do not express androgen receptor to respond directly to elevated androgens. A population of kisspeptin, neurokinin B, and dynorphin-expressing (KNDy) neurons in the hypothalamic arcuate nucleus are thought to regulate pulsatile GnRH release and some express androgen receptor. We did not find evidence, however, that PNA altered spontaneous activity of KNDy neurons before puberty at 3wks of age or in adulthood. This suggests that PNA likely acts through other components of the broader hypothalamic network to change the patterns of GnRH release.
Intelligent responses to our technological signals will not arrive in fewer than three millennia

Abstract What is the chance we start a conversation with another civilization like our own? Our technological society produced signals that could be received by other extraterrestrial civilizations, within a sphere around us with a radius of ∼ 1 0 2 light years. Given that, the Copernican principle provides a lower limit on the response time that we should expect from transmitters on Earth-like planets around Sun-like stars. If our civilization lives longer, the expected number of responses could increase. We explore the chance of detecting a response in the future, and show that a response should only be expected to arrive after a few millennia.