cssi/SciSciGPT-SciSciNet huggingface dataset:
From Toxicity in Online Comments to Incivility in American News: Proceed with Caution

The ability to quantify incivility online, in news and in congressional debates is of great interest to political scientists. Computational tools for detecting online incivility for English are now fairly accessible and potentially could be applied more broadly. We test the Jigsaw Perspective API for its ability to detect the degree of incivility on a corpus that we developed, consisting of manual annotations of civility in American news. We demonstrate that toxicity models, as exemplified by Perspective, are inadequate for the analysis of incivility in news. We carry out error analysis that points to the need to develop methods to remove spurious correlations between words often mentioned in the news, especially identity descriptors and incivility. Without such improvements, applying Perspective or similar models on news is likely to lead to wrong conclusions, that are not aligned with the human perception of incivility.
Random-Cluster Dynamics on Random Regular Graphs in Tree Uniqueness

We establish rapid mixing of the random-cluster Glauber dynamics on random $$\varDelta $$ -regular graphs for all $$q\ge 1$$ and $$p<p_u(q,\varDelta )$$ , where the threshold $$p_u(q,\varDelta )$$ corresponds to a uniqueness/non-uniqueness phase transition for the random-cluster model on the (infinite) $$\varDelta $$ -regular tree. It is expected that this threshold is sharp, and for $$q>2$$ the Glauber dynamics on random $$\varDelta $$ -regular graphs undergoes an exponential slowdown at $$p_u(q,\varDelta )$$ . More precisely, we show that for every $$q\ge 1$$ , $$\varDelta \ge 3$$ , and $$p<p_u(q,\varDelta )$$ , with probability $$1-o(1)$$ over the choice of a random $$\varDelta $$ -regular graph on n vertices, the Glauber dynamics for the random-cluster model has $$\varTheta (n \log n)$$ mixing time. As a corollary, we deduce fast mixing of the Swendsen–Wang dynamics for the Potts model on random $$\varDelta $$ -regular graphs for every $$q\ge 2$$ , in the tree uniqueness region. Our proof relies on a sharp bound on the “shattering time”, i.e., the number of steps required to break up any configuration into $$O(\log n)$$ sized clusters. This is established by analyzing a delicate and novel iterative scheme to simultaneously reveal the underlying random graph with clusters of the Glauber dynamics configuration on it, at a given time.
Extinction in complex communities as driven by adaptive dynamics

In a complex community, species continuously adapt to each other. On rare occasions, the adaptation of a species can lead to the extinction of others, and even its own. "Adaptive dynamics" is the standard mathematical framework to describe evolutionary changes in community interactions, and in particular, predict adaptation driven extinction. Unfortunately, most authors implement the equations of adaptive dynamics through computer simulations, that require assuming a large number of questionable parameters and fitness functions. In this study we present analytical solutions to adaptive dynamics equations, thereby clarifying how outcomes depend on any computational input. We develop general formulas that predict equilibrium abundances over evolutionary time scales. Additionally, we predict which species will go extinct next, and when this will happen.
The Milky Way Project: Probing Star Formation with First Results on Yellowballs from DR2

Yellowballs (YBs) were first discovered during the Milky Way Project citizen-science initiative (MWP; Simpson et al. 2012). MWP users noticed compact, yellow regions in Spitzer Space Telescope mid-infrared (MIR) images of the Milky Way plane and asked professional astronomers to explain these "yellow balls." Follow-up work by Kerton et al. (2015) determined that YBs likely trace compact photo-dissociation regions associated with massive and intermediate-mass star formation. YBs were included as target objects in a version of the Milky Way Project launched in 2016 (Jayasinghe et al. 2016), which produced a listing of over 6000 YB locations. We have measured distances, cross-match associations, physical properties, and MIR colors of ~500 YBs within a pilot region covering the l= 30 - 40 degrees, b= +/- 1 degree region of the Galactic plane. We find 20-30% of YBs in our pilot region contain high-mass star formation capable of becoming expanding H II regions that produce MIR bubbles. A majority of YBs represent intermediate-mass star-forming regions whose placement in evolutionary diagrams suggest they are still actively accreting, and may be precursors to optically-revealed Herbig Ae/Be nebulae. Many of these intermediate-mass YBs were missed by surveys of massive star-formation tracers and thus this catalog provides information for many new sites of star formation. Future work will expand this pilot region analysis to the entire YB catalog.
How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study

The development of AI applications is a multidisciplinary effort, involving multiple roles collaborating with the AI developers, an umbrella term we use to include data scientists and other AI-adjacent roles on the same team. During these collaborations, there is a knowledge mismatch between AI developers, who are skilled in data science, and external stakeholders who are typically not. This difference leads to communication gaps, and the onus falls on AI developers to explain data science concepts to their collaborators. In this paper, we report on a study including analyses of both interviews with AI developers and artifacts they produced for communication. Using the analytic lens of shared mental models, we report on the types of communication gaps that AI developers face, how AI developers communicate across disciplinary and organizational boundaries, and how they simultaneously manage issues regarding trust and expectations.
Private Returns to Public Investment: Political Career Incentives and Infrastructure Investment in China

Why do politicians who have short tenure expectations have incentives to invest in long-term infrastructure projects? This mismatch between politicians' short tenures and the long-term needed for infrastructure projects to come to fruition is generally expected to result in under-investment in critical infrastructure. However, recent data show that China makes massive investments in large-scale, long-term transportation projects. By proposing a political exchange model, we demonstrate a fundamental synergy between the incentives of short-term mayors and of provincial leaders that is realized as a result of subway projects. With both a difference-in-differences design and a fuzzy regression discontinuity design, we show that subway projects significantly increase the promotion chances of city mayors. Additional tests also confirm the mechanism of our theory. Mayors who obtain subway projects deliver economic benefits to provincial leaders. The provincial politicians' prospects of promotion are significantly improved thanks to these economic returns.
The Polar Flagellar Transcriptional Regulatory Network in Vibrio campbellii Deviates from Canonical Vibrio Species.

Swimming motility is a critical virulence factor in pathogenesis for numerous Vibrio species. Vibrio campbellii DS40M4 is a wild isolate that has been recently established as a highly tractable model strain for bacterial genetics studies. We sought to exploit the tractability and relevance of this strain for characterization of flagellar gene regulation in V. campbellii. Using comparative genomics, we identified homologs of V. campbellii flagellar and chemotaxis genes conserved in other members of the Vibrionaceae and determined the transcriptional profile of these loci using differential RNA-seq. We systematically deleted all 63 predicted flagellar and chemotaxis genes in V. campbellii and examined their effects on motility and flagellum production. We specifically focused on the core regulators of the flagellar hierarchy established in other vibrios: RpoN (σ54), FlrA, FlrC, and FliA. Our results show that V. campbellii transcription of flagellar and chemotaxis genes is governed by a multi-tiered regulatory hierarchy similar to other motile Vibrio species. However, there are several critical differences in V. campbellii: (i) the σ54-dependent regulator FlrA is dispensable for motility, (ii) the flgA, fliEFGHIJ, flrA, and flrBC operons do not require σ54 for expression, and (iii) FlrA and FlrC co-regulate class II genes. Our model proposes that the V. campbellii flagellar transcriptional hierarchy has three classes of genes, in contrast to the four-class hierarchy in Vibrio cholerae. Our genetic and phenotypic dissection of the V. campbellii flagellar regulatory network highlights the differences that have evolved in flagellar regulation across the Vibrionaceae. Importance Vibrio campbellii is a Gram-negative bacterium that is free-living and ubiquitous in marine environments and is an important global pathogen of fish and shellfish. Disruption of the flagellar motor significantly decreases host mortality of V. campbellii, suggesting that motility is a key factor in pathogenesis. Using this model organism, we identified >60 genes that encode proteins with predicted structural, mechanical, or regulatory roles in function of the single polar flagellum in V. campbellii. We systematically tested strains containing single deletions of each gene to determine the impact on motility and flagellum production. Our studies have uncovered differences in the regulatory network and function of several genes in V. campbellii as compared to established systems in Vibrio cholerae and Vibrio parahaemolyticus.
Epigenome-wide scan identifies differentially methylated regions for lung cancer using pre-diagnostic peripheral blood.

BACKGROUND DNA methylation markers have been associated with lung cancer risk and may identify aetiologically relevant genomic regions, or alternatively, be markers of disease risk factors or biological processes associated with disease development. METHODS In a nested case-control study, we measured blood leukocyte DNA methylation levels in pre-diagnostic samples collected from 430 participants (208 cases; 222 controls) in the 1989 CLUE II cohort. We compared DNA methylation levels with case/control status to identify novel genomic regions, both single CpG sites and differentially methylated regions (DMRs), while controlling for known DNA methylation changes associated with smoking using a previously described pack-years-based smoking methylation score. Stratification analyses were conducted over time from blood draw to diagnosis, histology, and smoking status. RESULTS We identified 16 single CpG sites and 40 DMRs significantly associated with lung cancer risk (q < 0.05). The identified genomic regions were associated with genes including H19, HOXA3/HOXA4, RUNX3, BRICD5, PLXNB2, and RP13. For the single CpG sites, the strongest association was noted for cg09736286 in the DIABLO gene (OR [for 1 SD] = 2.99, 95% CI: 1.95-4.59, P-value = 4.81 × 10-7). We found that CpG sites in the HOXA3/HOXA4 region were hypermethylated in cases compared to controls. CONCLUSION The single CpG sites and DMRs that we identified represented significant measurable differences in lung cancer risk, providing potential biomarkers for lung cancer risk stratification. Future studies will need to examine whether these regions are causally related to lung cancer.
Meta Learning for Support Recovery in High-dimensional Precision Matrix Estimation

In this paper, we study meta learning for support (i.e., the set of non-zero entries) recovery in high-dimensional precision matrix estimation where we reduce the sufficient sample complexity in a novel task with the information learned from other auxiliary tasks. In our setup, each task has a different random true precision matrix, each with a possibly different support. We assume that the union of the supports of all the true precision matrices (i.e., the true support union) is small in size. We propose to pool all the samples from different tasks, and \emph{improperly} estimate a single precision matrix by minimizing the $\ell_1$-regularized log-determinant Bregman divergence. We show that with high probability, the support of the \emph{improperly} estimated single precision matrix is equal to the true support union, provided a sufficient number of samples per task $n \in O((\log N)/K)$, for $N$-dimensional vectors and $K$ tasks. That is, one requires less samples per task when more tasks are available. We prove a matching information-theoretic lower bound for the necessary number of samples, which is $n \in \Omega((\log N)/K)$, and thus, our algorithm is minimax optimal. Then for the novel task, we prove that the minimization of the $\ell_1$-regularized log-determinant Bregman divergence with the additional constraint that the support is a subset of the estimated support union could reduce the sufficient sample complexity of successful support recovery to $O(\log(|S_{\text{off}}|))$ where $|S_{\text{off}}|$ is the number of off-diagonal elements in the support union and is much less than $N$ for sparse matrices. We also prove a matching information-theoretic lower bound of $\Omega(\log(|S_{\text{off}}|))$ for the necessary number of samples. Synthetic experiments validate our theory.
Synthesizing Linked Data Under Cardinality and Integrity Constraints

The generation of synthetic data is useful in multiple aspects, from testing applications to benchmarking to privacy preservation. Generating thelinks between relations, subject tocardinality constraints (CCs) andintegrity constraints (ICs) is an important aspect of this problem. Given instances of two relations, where one has a foreign key dependence on the other and is missing its foreign key ($FK$) values, and two types of constraints: (1) CCs that apply to the join view and (2) ICs that apply to the table with missing $FK$ values, our goal is to impute the missing $FK$ values such that the constraints are satisfied. We provide a novel framework for the problem based on declarative CCs and ICs. We further show that the problem is NP-hard and propose a novel two-phase solution that guarantees the satisfaction of the ICs. Phase I yields an intermediate solution accounting for the CCs alone, and relies on a hybrid approach based on CC types. For one type, the problem is modeled as an Integer Linear Program. For the others, we describe an efficient and accurate solution. We then combine the two solutions. Phase II augments this solution by incorporating the ICs and uses a coloring of the conflict hypergraph to infer the values of the $FK$ column. Our extensive experimental study shows that our solution scales well when the data and number of constraints increases. We further show that our solution maintains low error rates for the CCs.
Auditing for Diversity Using Representative Examples

Assessing the diversity of a dataset of information associated with people is crucial before using such data for downstream applications. For a given dataset, this often involves computing the imbalance or disparity in the empirical marginal distribution of a protected attribute (e.g. gender, dialect, etc.). However, real-world datasets, such as images from Google Search or collections of Twitter posts, often do not have protected attributes labeled. Consequently, to derive disparity measures for such datasets, the elements need to hand-labeled or crowd-annotated, which are expensive processes. We propose a cost-effective approach to approximate the disparity of a given unlabeled dataset, with respect to a protected attribute, using a control set of labeled representative examples. Our proposed algorithm uses the pairwise similarity between elements in the dataset and elements in the control set to effectively bootstrap an approximation to the disparity of the dataset. Importantly, we show that using a control set whose size is much smaller than the size of the dataset is sufficient to achieve a small approximation error. Further, based on our theoretical framework, we also provide an algorithm to construct adaptive control sets that achieve smaller approximation errors than randomly chosen control sets. Simulations on two image datasets and one Twitter dataset demonstrate the efficacy of our approach (using random and adaptive control sets) in auditing the diversity of a wide variety of datasets.
Relativistic spacetime crystals

Periodic space crystals are well established and widely used in physical sciences. Time crystals have been increasingly explored more recently, where time is disconnected from space. Periodic relativistic spacetime crystals on the other hand need to account for the mixing of space and time in special relativity through Lorentz transformation, and have been listed only in 2D. This work shows that there exists a transformation between the conventional Minkowski spacetime (MS) and what is referred to here as renormalized blended spacetime (RBS); they are shown to be equivalent descriptions of relativistic physics in flat spacetime. There are two elements to this reformulation of MS, namely, blending and renormalization. When observers in two inertial frames adopt each other's clocks as their own, while retaining their original space coordinates, the observers become blended. This process reformulates the Lorentz boosts into Euclidean rotations while retaining the original spacetime hyperbola describing worldlines of constant spacetime length from the origin. By renormalizing the blended coordinates with an appropriate factor that is a function of the relative velocities between the various frames, the hyperbola is transformed into a Euclidean circle. With these two steps, one obtains the RBS coordinates complete with new light lines, but now with a Euclidean construction. One can now enumerate the RBS point and space groups in various dimensions with their mapping to the well known space crystal groups. The RBS point group for flat isotropic RBS spacetime is identified to be that of cylinders in various dimensions: mm2 which is that of a rectangle in 2D, (∞/m)m which is that of a cylinder in 3D, and that of a hypercylinder in 4D. An antisymmetry operation is introduced that can swap between space-like and time-like directions, leading to color spacetime groups. The formalism reveals RBS symmetries that are not readily apparent in the conventional MS formulation. Mathematica script is provided for plotting the MS and RBS geometries discussed in the work.
On the Lagrangian turbulence in continua

Lagrangian turbulence in continua is introduced as a failure of Euclidian metric of a material system of coordinates, and it is shown that the Ricci tensor of such a system of coordinates may be used as a measure of L-turbulence. The conditions which are sufficient, though not necessary, for instability of an initially infinitesimal disturbance of this tensor are determined for fluid, elastic, and viscoelastic bodies. The results are obtained from the linearized equations, and therefore the growth of unstable solutions may be bounded.
Endotoxin enhances microvascular thrombosis in mouse cremaster venules via a TLR4‐dependent, neutrophil‐independent mechanism

Endotoxemia promotes adhesive interactions between platelets and microvascular endothelium in vivo. We sought to determine whether endotoxin (lipopolysaccharide, LPS) modified platelet thrombus for...
GPU-aware Communication with UCX in Parallel Programming Models: Charm++, MPI, and Python

As an increasing number of leadership-class systems embrace GPU accelerators in the race towards exascale, efficient communication of GPU data is becoming one of the most critical components of high-performance computing. For developers of parallel programming models, implementing support for GPU-aware communication using native APIs for GPUs such as CUDA can be a daunting task as it requires considerable effort with little guarantee of performance. In this work, we demonstrate the capability of the Unified Communication X (UCX) framework to compose a GPU-aware communication layer that serves multiple parallel programming models of the Charm++ ecosystem: Charm++, Adaptive MPI (AMPI), and Charm4py. We demonstrate the performance impact of our designs with microbenchmarks adapted from the OSU benchmark suite, obtaining improvements in latency of up to 10.2x, 11.7x, and 17.4x in Charm++, AMPI, and Charm4py, respectively. We also observe increases in bandwidth of up to 9.6x in Charm++, 10x in AMPI, and 10.5x in Charm4py. We show the potential impact of our designs on real-world applications by evaluating a proxy application for the Jacobi iterative method, improving the communication performance by up to 12.4x in Charm++, 12.8x in AMPI, and 19.7x in Charm4py.
Regulation of the normalized rate of driven magnetic reconnection through shocked flux pileup

Magnetic reconnection is explored on the Terrestrial Reconnection Experiment (TREX) for asymmetric inflow conditions and in a configuration where the absolute rate of reconnection is set by an external drive. Magnetic pileup enhances the upstream magnetic field of the high-density inflow, leading to an increased upstream Alfven speed and helping to lower the normalized reconnection rate to values expected from theoretical consideration. In addition, a shock interface between the far upstream supersonic plasma inflow and the region of magnetic flux pileup is observed, important to the overall force balance of the system, thereby demonstrating the role of shock formation for configurations including a supersonically driven inflow. Despite the specialized geometry where a strong reconnection drive is applied from only one side of the reconnection layer, previous numerical and theoretical results remain robust and are shown to accurately predict the normalized rate of reconnection for the range of system sizes considered. This experimental rate of reconnection is dependent on system size, reaching values as high as 0.8 at the smallest normalized system size applied.
A Quality Diversity Approach to Automatically Generating Human-Robot Interaction Scenarios in Shared Autonomy

The growth of scale and complexity of interactions between humans and robots highlights the need for new computational methods to automatically evaluate novel algorithms and applications. Exploring diverse scenarios of humans and robots interacting in simulation can improve understanding of the robotic system and avoid potentially costly failures in real-world settings. We formulate this problem as a quality diversity (QD) problem, where the goal is to discover diverse failure scenarios by simultaneously exploring both environments and human actions. We focus on the shared autonomy domain, where the robot attempts to infer the goal of a human operator, and adopt the QD algorithm MAP-Elites to generate scenarios for two published algorithms in this domain: shared autonomy via hindsight optimization and linear policy blending. Some of the generated scenarios confirm previous theoretical findings, while others are surprising and bring about a new understanding of state-of-the-art implementations. Our experiments show that MAP-Elites outperforms Monte-Carlo simulation and optimization based methods in effectively searching the scenario space, highlighting its promise for automatic evaluation of algorithms in human-robot interaction.
Reducible Fermi Surface for Multi-layer Quantum Graphs Including Stacked Graphene

We construct two types of multi-layer quantum graphs (Schrodinger operators on metric graphs) for which the dispersion function of wave vector and energy is proved to be a polynomial in the dispersion function of the single layer. This leads to the reducibility of the algebraic Fermi surface, at any energy, into several components. Each component contributes a set of bands to the spectrum of the graph operator. When the layers are graphene, AA-, AB-, and ABC-stacking are allowed within the same multi-layer structure. One of the tools we introduce is a surgery-type calculus for obtaining the dispersion function for a periodic quantum graph by joining two graphs together. Reducibility of the Fermi surface allows for the construction of local defects that engender bound states at energies embedded in the radiation continuum.
Exclusion and multiplicity for stable communities in Lotka-Volterra systems.

For classic Lotka–Volterra systems governing many interacting species, we establish an exclusion principle that rules out the existence of linearly asymptotically stable steady states in subcommunities of communities that admit a stable state which is internally D-stable. This type of stability is known to be ensured, e.g., by diagonal dominance or Volterra–Lyapunov stability conditions. By consequence, the number of stable steady states of this type is bounded by Sperner’s lemma on anti-chains in a poset. The number of stable steady states can nevertheless be very large if there are many groups of species that strongly inhibit outsiders but have weak interactions among themselves. By examples we also show that in general it is possible for a stable community to contain a stable subcommunity consisting of a single species. Thus a recent empirical finding to the contrary, in a study of random competitive systems by Lischke and Loffler (Theor Popul Biol 115:24–34, 2017), does not hold without qualification.
Efficient neural network implementation of morphological operations

This paper introduces a neural network implementation of gray scale operators. In this structure, synaptic weights are represented by a gray scale structuring element and trained by a learning algorithm based on an optimal criterion called the overall equality index. The proposed algorithm leads to a computationally simple implementation, with numerical examples to illustrate its performance.
A MACHINE LEARNING DISTRACTED DRIVING PREDICTION MODEL

Distracted driving is known to be one of the core contributors to crashes in the U.S., accounting for about 40% of all crashes. Drivers' situational awareness, decision-making, and driving performance are impaired due to temporarily diverting their attention from the primary task of driving to other tasks not related to driving. Detecting driver distraction would help in adapting the most effective countermeasures. To find the best strategies to overcome this problem, we developed a Bayesian Network (BN) distracted driving prediction model using a driving simulator. In this study, we use a Bayesian Network classifier as a robust machine learning algorithm on our trained data (80%) and tested (20%) with the data collected from a driving simulator, in which the 92 participants drove six scenarios of handheld calling, hands-free calling, texting, voice command, clothing, and eating/drinking on four different road classes (rural collector, freeway, urban arterial, and local road in a school zone). Various driving performances such as speed, acceleration, throttle, lane changing, brake, collision, and offset from the lane center were investigated. Here we investigated different optimization models to build the best BN in which a Genetic Search Algorithm obtained the best performance. As a result, we achieved a 67.8% prediction accuracy using our model to predict driver distraction. We also conducted a 62.6% true positive rate, which demonstrates the ability of our model to predict distractions correctly.
NetCut: Real-Time DNN Inference Using Layer Removal

Deep Learning plays a significant role in assisting humans in many aspects of their lives. As these networks tend to get deeper over time, they extract more features to increase accuracy at the cost of additional inference latency. This accuracy-performance trade-off makes it more challenging for Embedded Systems, as resource-constrained processors with strict deadlines, to deploy them efficiently. This can lead to selection of networks that can prematurely meet a specified deadline with excess slack time that could have potentially contributed to increased accuracy. In this work, we propose: (i) the concept of layer removal as a means of constructing TRimmed Networks (TRNs) that are based on removing problem-specific features of a pretrained network used in transfer learning, and (ii) NetCut, a methodology based on an empirical or an analytical latency estimator, which only proposes and retrains TRNs that can meet the application's deadline, hence reducing the exploration time significantly. We demonstrate that TRNs can expand the Pareto frontier that trades off latency and accuracy to provide networks that can meet arbitrary deadlines with potential accuracy improvement over off-the-shelf networks. Our experimental results show that such utilization of TRNs, while transferring to a simpler dataset, in combination with NetCut, can lead to the proposal of networks that can achieve relative accuracy improvement of up to 10.43% among existing off-the-shelf neural architectures while meeting a specific deadline, and 27x speedup in exploration time.
Improving the Fidelity of Mixed-Monotone Reachable Set Approximations via State Transformations

Mixed-monotone systems are separable via a decomposition function into increasing and decreasing components, and this decomposition function allows for embedding the system dynamics in a higher-order monotone embedding system. Embedding the system dynamics in this way facilitates the efficient over-approximation of reachable sets with hyper-rectangles, however, unlike the monotonicity property, which can be applied to compute, e.g., the tightest hyperrectangle containing a reachable set, the application of the mixed-monotonicity property generally results in conservative reachable set approximations. In this work, we explore conservatism in the method, and we consider, in particular, embedding systems that are monotone with respect to an alternative partial order. This alternate embedding system is constructed with a decomposition function for a related system formed via a linear transformation of the initial state-space. We show how these alternate embedding systems allow for computing reachable sets with improved fidelity, i.e., reduced conservatism.
Herding an Adversarial Swarm in Three-dimensional Spaces

This paper presents a defense approach to safeguard a protected area against an attack by a swarm of adversarial agents in three-dimensional (3D) space. We extend our 2D ‘stringNet Herding’ approach, in which a closed formation of string-barriers is established around the adversarial swarm to confine their motion and herd them to a safe area, to 3D spaces by introducing 3D-StringNet. 3D-StringNet is a closed 3D formation of triangular netlike barriers. We provide a systematic approach to generate three types of 3D formations that are used in the 3D herding process and modifications to the finite-time convergent control laws developed in our earlier work. Furthermore, for given initial positions of the defenders, we provide conditions on the initial positions of the attackers for which the defenders are guaranteed to gather as a specified formation at a position on the shortest path of the attackers to the protected area before attackers reach there. The approach is investigated in simulations.
Triggering GaAs lock-on switches with laser diode arrays

Progress toward the triggering of high-power photoconductive semiconductor switches (PCSSs) with laser diode arrays, is reported. An 850-W optical pulse from a laser diode array was used to trigger a 1.5-cm-long switch that delivered 8.5 MW to a 38.3- Omega load. Using 166-W arrays, it was possible to trigger a 2.5-mm-long switch delivering 1.2 MW with 600-ps rise-times at pulse repetition frequencies of 1 kHz. These 2.5-mm-long switches survived 10/sup 5/ pulses at 1.0 MW levels. In single-pulse operation, up to 600 A was switched with laser diode arrays. The goal is to switch up to 5 kA in a single-shot mode and up to 100 MW repetitively at up to 10 kHz. At electric fields below 3 kV/cm GaAs switches are activated by creation of one electron-hole pair per photon. This linear mode demands high laser power and, after the light pulse, the carriers recombine in nanoseconds. At higher electric fields GaAs acts as a light-activated Zener diode. The laser light generates carriers as before, but the field induces gain such that the amount of light required to trigger the switch is reduced by a factor of up to 500. The gain continues until the field across the sample drops to a material-dependent lock-on field. The gain in the switch allows for the use of laser diodes. >
Bounding Infection Prevalence by Bounding Selectivity and Accuracy of Tests: With Application to Early COVID-19

I propose novel partial identification bounds on infection prevalence from information on test rate and test yield. The approach utilizes user-specified bounds on (i) test accuracy and (ii) the extent to which tests are targeted, formalized as restriction on the effect of true infection status on the odds ratio of getting tested and thereby embeddable in logit specifications. The motivating application is to the COVID-19 pandemic but the strategy may also be useful elsewhere. Evaluated on data from the pandemic's early stage, even the weakest of the novel bounds are reasonably informative. Notably, and in contrast to speculations that were widely reported at the time, they place the infection fatality rate for Italy well above the one of influenza by mid-April.
Unveiling the Population of Wandering Black Holes via Electromagnetic Signatures

While most galaxies appear to host a central supermassive black hole (SMBH), they are expected to also contain a substantial population of off-center "wandering" SMBHs naturally produced by the hierarchical merger-driven process of galaxy assembly. This population has been recently characterized in an analysis of the Romulus cosmological simulations, which correct for the dynamical forces on SMBHs without artificially pinning them to halo centers. Here we predict an array of electromagnetic signatures for these wanderers. The predicted wandering population of SMBHs from Romulus broadly reproduces the observed spatial offsets of a recent sample of hyperluminous X-ray sources. We predict that the sources with the most extreme offsets are likely to arise from SMBHs within satellite galaxies. These simulations also predict a significant population of secondary active galactic nuclei (AGN) with luminosities at least 10\% that of the central AGN. The majority of galaxies at $z=4$ that host a central AGN with bolometric luminosity $L_\mathrm{bol}>10^{42} \ \mathrm{erg} \; \mathrm{s}^{-1}$ are predicted to host a companion off-center AGN of comparable brightness. We demonstrate that stacked X-ray observations of similar mass galaxies may reveal a halo of collective emission attributable to these wanderers. Finally, because wanderers dominate the population of SMBHs with masses of $\lesssim 10^7\,M_{\odot}$ in Romulus, they may dominate tidal disruption event (TDE) rates at these masses if they retain a stellar component (e.g. a nuclear star cluster). This could warrant an order of magnitude correction to current theoretically estimated TDE rates at low SMBH masses.
Minimalistic 3D chromatin models: Sparse interactions in single cells drive the chromatin fold and form many-body units.

Abstract Computational three-dimensional chromatin modeling has helped uncover principles of genome organization. Here, we discuss methods for modeling three-dimensional chromatin structures, with focus on a minimalistic polymer model which inverts population Hi-C into single-cell conformations. Utilizing only basic physical properties, this model reveals that a few specific Hi-C interactions can fold chromatin into conformations consistent with single-cell imaging, Dip-C, and FISH measurements. Aggregated single-cell chromatin conformations also reproduce Hi-C frequencies. This approach allows quantification of structural heterogeneity and discovery of many-body interaction units and has revealed additional insights, including (1) topologically associating domains as a byproduct of folding driven by specific interactions, (2) cell subpopulations with different structural scaffolds are developmental stage dependent, and (3) the functional landscape of many-body units within enhancer-rich regions. We also discuss these findings in relation to the genome structure–function relationship.
Chlorine inactivation of Escherichia coli O157:H7 in fresh produce wash process: effectiveness and modeling

Abstract Inactivation rate constant or inactivation coefficient (specific lethality) quantifies the rate at which a chemical sanitizer inactivates a microorganism. This study presents a modified disinfection kinetics model to evaluate the potential effect of organic content on the chlorine inactivation coefficient of Escherichia coli O157:H7 in fresh produce wash processes. Results show a significant decrease in the bactericidal efficacy of free chlorine (FC) in the presence of organic load compared to its absence. While the chlorine inactivation coefficient of Escherichia coli O157:H7 is 70.39 ± 3.19 L/mg/min in the absence of organic content, it drops by 73% for a chemical oxygen demand (COD) level of 600–800 mg/L. Results also indicate that the initial chlorine concentration and bacterial load have no effect on the chlorine inactivation coefficient. A second-order chemical reaction model for FC decay, which utilizes a proportion of COD as an indicator of organic content in fresh produce wash was employed, yielding an apparent reaction rate of (9.45 ± 0.22) × 10−4 /μM/min. This model was validated by predicting FC concentration in multi-run continuous wash cycles with periodic replenishment of chlorine.
Soft pions and transport near the chiral critical point

During the expansion of a heavy ion collision, the system passes close to the $O(4)$ critical point of QCD, and thus the fluctuations of the order parameter $(\ensuremath{\sigma},\stackrel{\ensuremath{\rightarrow}}{\ensuremath{\pi}})$ are expected to be enhanced. Our goal is to compute how these enhanced fluctuations modify the transport coefficients of QCD near the pseudocritical point. We also make a phenomenological estimate for how chiral fluctuations could effect the momentum spectrum of soft pions. We first formulate the appropriate stochastic hydrodynamic equations close to the $O(4)$ critical point. Then, working in mean field, we determine the correlation functions of the stress tensor and the currents which result from this stochastic real-time theory, and use these correlation functions to determine the scaling behavior of the transport coefficients. The hydrodynamic theory also describes the propagation of pion waves, fixing the scaling behavior of the dispersion curve of soft pions. We present scaling functions for the shear viscosity and the charge conductivities near the pseudocritical point and estimate the absolute magnitude of the critical fluctuations to these parameters and the bulk viscosity. Using the calculated pion dispersion curve, we estimate the expected critical enhancement of soft pion yields, and this estimate provides a plausible explanation for the excess seen in experiment relative to ordinary hydrodynamic computations. Our results motivate further phenomenological and numerical work on the implications of chiral symmetry on real-time properties of thermal QCD near the pseudocritical point.
In-flight deflection measurement of the HiMAT aeroelastically tailored wing

An electro-optical flight deflection measurement system was developed for NASA for use on the highly maneuverable aircraft technology (HiMAT) remotely piloted research vehicle (RPRV) to provide a means of evaluating the performance of the HiMAT's aeroelastically tailored composite wing and canard. A description of the flight deflection measurement system is presented from a user's viewpoint and includes the general method of operation, system capabilities and limitations, method of installation on the HiMAT vehicle, and calibration of targets. Also included is a general description of the HiMAT RPRV and its design goals. Preliminary flight deflection and bending moment data were obtained at Mach 0.8 and were extrapolated to the Mach 0.9 maneuver design condition for comparison to NASTRAN predictions and ground loads test results. The preliminary flight test results tended to agree with the results obtained from the static ground loads tests, that is, that the NASTRAN model overpredicted the streamwise twist of the composite outer wing panel.
Efficient Inhibition of HIV Using CRISPR/Cas13d Nuclease System

Recently discovered Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas13 proteins are programmable RNA-guided ribonucleases that target single-stranded RNA (ssRNA). CRISPR/Cas13-mediated RNA targeting has emerged as a powerful tool for detecting and eliminating RNA viruses. Here, we demonstrate the effectiveness of CRISPR/Cas13d to inhibit HIV-1 replication. We designed guide RNAs (gRNAs) targeting highly conserved regions of HIV-1. RfxCas13d (CasRx) in combination with HIV-specific gRNAs efficiently inhibited HIV-1 replication in cell line models. Furthermore, simultaneous targeting of four distinct, non-overlapping sites in the HIV-1 transcript resulted in robust inhibition of HIV-1 replication. We also show the effective HIV-1 inhibition in primary CD4+ T-cells and suppression of HIV-1 reactivated from latently infected cells using the CRISPR/Cas13d system. Our study demonstrates the utility of the CRISPR/Cas13d nuclease system to target acute and latent HIV infection and provides an alternative treatment modality against HIV.
Improved Quantitative Regularity for the Navier–Stokes Equations in a Scale of Critical Spaces

We prove a quantitative regularity theorem and blowup criterion for classical solutions of the three-dimensional Navier–Stokes equations satisfying certain critical conditions. The solutions we consider have $$\Vert r^{1-\frac{3}{q}}u\Vert _{L_t^\infty L_x^q}<\infty $$ where $$r=\sqrt{x_1^2+x_2^2}$$ and either $$q\in (3,\infty )$$ , or u is axisymmetric and $$q\in (2,3]$$ . Using the strategy of Tao (Quantitative bounds for critically bounded solutions to the Navier–Stokes equations. arXiv:1908.04958 , 2019), we obtain improved subcritical estimates for such solutions depending only on the double exponential of the critical norm. One consequence is a double logarithmic lower bound on the blowup rate. We make use of some tools such as a decomposition of the solution that allows us to use energy methods in these spaces, as well as a Carleman inequality for the heat equation suited for proving quantitative backward uniqueness in cylindrical regions.
ARA: A Wireless Living Lab Vision for Smart and Connected Rural Communities

The rural US includes 72% of the nation's land and 46 million people, and it serves as major sources of food and energy for the nation. Thus rural prosperity is essential to US wellbeing. As a foundation for next-generation rural economy and communities, broadband connectivity is a key driver of rural prosperity. Yet 39% of the rural US lacks broadband access, and most agriculture (ag) farms are not connected at all. To address the rural broadband challenge, we will develop the ARA rural wireless living lab. ARA will not only serve as a first-of-its-kind, real-world wireless experimental infrastructure for smart and connected rural communities, it will also provide the living lab processes, activities, and organizations to engage the broad wireless and application communities in the research, education, innovation, and pilot of affordable, high-capacity rural broadband solutions. Through this visioning article, we illustrate the community, application, economic, and operational contexts of rural wireless, the design of ARA, ARA-enabled research, and how ARA is expected to make rural broadband as affordable as urban broadband today. This article is also a call-to-action for the broad wireless and application communities to participate in the ARA living lab activities and to join the ARA Consortium of public-private partners in shaping the future of advanced wireless, rural broadband, and rural communities in general.
An investigation of high entropy alloy conductivity using first-principles calculations

The Kubo–Greenwood equation, in combination with the first-principles Korringa–Kohn–Rostoker coherent potential approximation (KKR-CPA) can be used to calculate the DC residual resistivity of random alloys at T = 0 K. We implemented this method in a multiple scattering theory based ab initio package, MuST, and applied it to the ab initio study of the residual resistivity of the high entropy alloy AlxCoCrFeNi as a function of x. The calculated resistivities are compared with experimental data. We also predict the residual resistivity of refractory high entropy alloy MoNbTaVxW. The calculated resistivity trends are also explained using theoretical arguments.
Misallocation or Mismeasurement

Abstract The ratio of revenue to inputs differs greatly across plants within countries such as the U.S. and India. Such gaps may reflect misallocation which lowers aggregate productivity. But differences in measured average products need not reflect differences in true marginal products. We propose a way to estimate the gaps in true marginal products in the presence of measurement error. Our method exploits how revenue growth is less sensitive to input growth when a plant’s average products are overstated by measurement error. For Indian manufacturing from 1985 to 2013, our correction lowers potential gains from reallocation by 20%. For the U.S. the effect is even more dramatic, reducing potential gains by 60% and eliminating 2/3 of a severe downward trend in allocative efficiency over 1978 to 2013.
Understanding and Evaluating Racial Biases in Image Captioning

Image captioning is an important task for benchmarking visual reasoning and for enabling accessibility for people with vision impairments. However, as in many machine learning settings, social biases can influence image captioning in undesirable ways. In this work, we study bias propagation pathways within image captioning, focusing specifically on the COCO dataset. Prior work has analyzed gender bias in captions using automatically-derived gender labels; here we examine racial and intersectional biases using manual annotations. Our first contribution is in annotating the perceived gender and skin color of 28,315 of the depicted people after obtaining IRB approval. Using these annotations, we compare racial biases present in both manual and automatically-generated image captions. We demonstrate differences in caption performance, sentiment, and word choice between images of lighter versus darker-skinned people. Further, we find the magnitude of these differences to be greater in modern captioning systems compared to older ones, thus leading to concerns that without proper consideration and mitigation these differences will only become increasingly prevalent. Code and data is available at this https URL .
An integral-free representation of the Dyson series using divided differences

The Dyson series is an infinite sum of multi-dimensional time-ordered integrals, which serves as a formal representation of the quantum time evolution operator in the interaction picture. Using the mathematical tool of divided differences, we introduce an alternative representation for the series that is entirely free from both time ordering and integrals. In this new formalism, the Dyson expansion is given as a sum of efficiently-computable divided differences of the exponential function, considerably simplifying the calculation of the Dyson expansion terms, while also allowing for time-dependent perturbation calculations to be performed directly in the Schr{o}dinger picture. We showcase the utility of this novel representation by studying a number of use cases. We also discuss several immediate applications.