cssi/SciSciGPT-SciSciNet huggingface dataset:
Allele frequency differentiation at height-associated SNPs among continental human populations.

Methods to detect polygenic adaptation have recently been shown to be sensitive to uncorrected stratification in GWAS, thereby casting doubts on whether polygenic adaptation is prevalent among humans. Consistent with a signal of adaptation at human height loci, the mean FST among African, East Asian, and European populations was shown to be significantly higher at height-associated SNPs than that at non-associated SNPs. This conclusion was reached, however, using height-associated SNPs ascertained from a GWAS design impacted by residual confounding due to uncorrected stratification. Specifically, we show here that the estimated effect sizes are significantly correlated with population structure across continents, potentially explaining the elevated differentiation previously reported. We alleviated these concerns of confounding by ascertaining height-associated SNPs from two biobank GWAS (UK Biobank, UKB, and Biobank Japan, BBJ), where measures to control for confounding in GWAS are more effective. Consistent with a global signature of polygenic adaptation, we found that compared to non-associated SNPs, frequencies of height-associated SNPs are indeed significantly more differentiated among continental populations from both the 1000 Genomes Project (p = 0.0012 for UKB and p = 0.0265 for BBJ), and the Human Genome Diversity Project (p = 0.0225 for UKB and p = 0.0032 for BBJ). However, we found no significant difference among continental populations in polygenic height scores. Through simulations, we found that polygenic score-based statistics could lose power in detecting polygenic adaptation in presence of independent converging selections, thereby potentially explaining the inconsistent results based on FST and polygenic scores.
Wireless Link Scheduling via Interference-aware Symmetric Positive Definite Connectivity Manifolds

In this paper, we investigate the fundamental problem of wireless link scheduling in device-to-device (D2D) networks, through the lens of Riemannian geometry. Our goal is to find a novel metric to characterize interference among D2D pairs, which can pave the way towards efficient and fast scheduling algorithms. Towards achieving this goal, we first model the connectivity pattern of each D2D pair, including its interference links, as a positively-shifted Laplacian matrix, which is a symmetric positive definite (SPD) one. Noting that SPD matrices constitute a non-Euclidean manifold, we represent each of the D2D pairs as a point on the SPD (i.e., conic) manifold, which is analyzed via Riemannian geometry. Accordingly we employ Riemannian metrics (e.g., Log-Euclidean metric "LEM"), which are suitable measures of distances on manifolds, to characterize the interference among D2D points on the SPD manifold.To validate the effectiveness of the proposed LEM-based interference measure, we propose a sequential link selection algorithm that schedules D2D pairs in a descending order of their signal-to-noise ratio (SNR), while keeping their LEM distances towards the already-scheduled pairs on the Riemannian manifold to be greater than a certain LEM threshold. Such LEM-based condition is equivalent to limiting the interference from potential D2D pairs to be below certain threshold. We show that the proposed LEM-based scheduling algorithm achieves sum rate of more than 86% of state-of-the-art ones (e.g., FPLinQ [1]), while only requiring spatial locations of D2D pairs, as opposed to requiring full channel state information (CSI).
Adaptability and the Pivot Penalty in Science

The ability to confront new questions, opportunities, and challenges is of fundamental importance to human progress and the resilience of human societies, yet the capacity of science to meet new demands remains poorly understood. Here we deploy a new measurement framework to investigate the scientific response to the COVID-19 pandemic and the adaptability of science as a whole. We find that science rapidly shifted to engage COVID-19 following the advent of the virus, with scientists across all fields making large jumps from their prior research streams. However, this adaptive response reveals a pervasive “pivot penalty,” where the impact of the new research steeply declines the further the scientists move from their prior work. The pivot penalty is severe amidst COVID-19 research, but it is not unique to COVID-19. Rather it applies nearly universally across the sciences, and has been growing in magnitude over the past five decades. While further features condition pivoting, including a scientist’s career stage, prior expertise and impact, collaborative scale, the use of new coauthors, and funding, we find that the pivot penalty persists and remains substantial regardless of these features, suggesting the pivot penalty acts as a fundamental friction that governs science’s ability to adapt. The pivot penalty not only holds key implications for the design of the scientific system and human capacity to confront emergent challenges through scientific advance, but may also be relevant to other social and economic systems, where shifting to meet new demands is central to survival and success.
Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling

We provide a new convergence analysis of stochastic gradient Langevin dynamics (SGLD) for sampling from a class of distributions that can be non-log-concave. At the core of our approach is a novel conductance analysis of SGLD using an auxiliary time-reversible Markov Chain. Under certain conditions on the target distribution, we prove that $\tilde O(d^4\epsilon^{-2})$ stochastic gradient evaluations suffice to guarantee $\epsilon$-sampling error in terms of the total variation distance, where $d$ is the problem dimension. This improves existing results on the convergence rate of SGLD \citep{raginsky2017non,xu2018global}. We further show that provided an additional Hessian Lipschitz condition on the log-density function, SGLD is guaranteed to achieve $\epsilon$-sampling error within $\tilde O(d^{15/4}\epsilon^{-3/2})$ stochastic gradient evaluations. Our proof technique provides a new way to study the convergence of Langevin based algorithms, and sheds some light on the design of fast stochastic gradient based sampling algorithms.
Variable Decomposition for Prophet Inequalities and Optimal Ordering

We introduce a new decomposition technique for random variables that maps a generic instance of the prophet inequalities problem to a new instance where all but a constant number of variables have a tractable structure that we refer to as (e, δ)-smallness. Using this technique, we make progress on several outstanding problems in the area: We show that, even in the case of non-identical distributions, it is possible to achieve (arbitrarily close to) the optimal approximation ratio of β ~0.745 when the items arrive in a random order (this version is commonly known as prophet secretary) as long as we are allowed to remove a small constant number of distributions. We show that forfrequent instances (where each distribution reoccurs some number of times) and random arrival order, it is possible to achieve the optimal approximation ratio of β (improving over the previous best-known bound of 0.738). We give a new, simpler proof of Kertz's optimal approximation guarantee of β ~0.745 for prophet inequalities with i.i.d. distributions. The proof is primal-dual and simultaneously produces upper and lower bounds. Using this decomposition in combination with a novel convex programming formulation, we construct the first (in parallel work with[1]) an Efficient PTAS (EPTAS) for the Optimal Ordering problem.
Speech Driven Talking Face Generation from a Single Image and an Emotion Condition

Visual emotion expression plays an important role in audiovisual speech communication. In this work, we propose a novel approach to rendering visual emotion expression in speech-driven talking face generation. Specifically, we design an end-to-end talking face generation system that takes a speech utterance, a single face image, and a categorical emotion label as input to render a talking face video synchronized with the speech and expressing the conditioned emotion. Objective evaluation on image quality, audiovisual synchronization, and visual emotion expression shows that the proposed system outperforms a state-of-the-art baseline system. Subjective evaluation of visual emotion expression and video realness also demonstrates the superiority of the proposed system. Furthermore, we conduct a human emotion recognition pilot study using generated videos with mismatched emotions among the audio and visual modalities. Results show that humans respond to the visual modality more significantly than the audio modality on this task.
A numerical evaluation of icing effects on a natural laminar flow airfoil

As a part of CFD code validation efforts within the Icing Branch of NASA Glenn Research Center, computations were performed for natural laminar flow (NLF) airfoil, NLF-0414. with 6 and 22.5 minute ice accretions. Both 3-D ice castings and 2-D machine-generated ice shapes were used in wind tunnel tests to study the effects of natural ice is well as simulated ice. They were mounted in the test section of the Low Turbulence Pressure Tunnel (LTPT) at NASA Langley that the 2-dimensionality of the flow can be maintained. Aerodynamic properties predicted by computations were compared to data obtained through the experiment by the authors at the LTPT. Computations were performed only in 2-D and in the case of 3-D ice, the digitized ice shape obtained at one spanwise location was used. The comparisons were mainly concentrated on the lift characteristics over Reynolds numbers ranging from 3 to 10 million and Mach numbers ranging from 0.12 to 0.29. WIND code computations indicated that the predicted stall angles were in agreement with experiment within one or two degrees. The maximum lift values obtained by computations were in good agreement with those of the experiment for the 6 minute ice shapes and the minute 3-D ice, but were somewhat lower in the case of the 22.5 minute 2-D ice. In general, the Reynolds number variation did not cause much change in the lift values while the variation of Mach number showed more change in the lift. The Spalart-Allmaras (S-A) turbulence model was the best performing model for the airfoil with the 22.5 minute ice and the Shear Stress Turbulence (SST) turbulence model was the best for the airfoil with the 6 minute ice and also for the clean airfoil. The pressure distribution on the surface of the iced airfoil showed good agreement for the 6 minute ice. However, relatively poor agreement of the pressure distribution on the upper surface aft of the leading edge horn for the 22.5 minute ice suggests that improvements are needed in the grid or turbulence models.
The Surface of (16) Psyche from Thermal Emission and Polarization Mapping

The asteroid (16) Psyche is the largest of the M-type asteroids, which have been hypothesized to be the cores of disrupted planetesimals and the parent bodies of the iron meteorites. While recent evidence has collected against a pure metal composition for Psyche, its spectrum and radar properties remain anomalous. We observed (16) Psyche in thermal emission with the Atacama Large (sub-)Millimeter Array (ALMA) at a resolution of 30 km over 2/3 of its rotation. The diurnal temperature variations are at the $\sim$10 K level over most of the surface and are best fit by a smooth surface with a thermal inertia of 280$\pm$100 J m$^{-2}$ K$^{-1}$ s$^{-1/2}$. We measure a millimeter emissivity of 0.61$\pm$0.02, which we interpret via a model that treats the surface as a porous mixture of silicates and metals, where the latter may take the form of iron sulfides/oxides or alternatively as conducting metallic inclusions. The emissivity indicates a metal content of no less than 20\% and potentially much higher, but the polarized emission that should be present for a surface with $\geq$20\% metal content is almost completely absent. This requires a highly scattering surface, which may be due to the presence of reflective metallic inclusions. If such is the case, a consequence is that metal-rich asteroids may produce less polarized emission than metal-poor asteroids, exactly the opposite prediction from standard theory, arising from the dominance of scattering over the bulk material properties.
Linear nearest neighbor flocks with all distinct agents

This paper analyzes the global dynamics of one-dimensional agent arrays with nearest neighbor linear couplings. The equations of motion are second-order linear ODE’s with constant coefficients. The novel part of this research is that the couplings are different for each distinct agent. We allow the forces to depend on the positions and velocity (damping terms) but the magnitudes of both the position and velocity couplings are different for each agent. We, also, do not assume that the forces are “Newtonian” (i.e. the force due to A on B equals the minus the force of B on A) as this assumption does not apply to certain situations, such as traffic modeling. For example, driver A reacting to driver B does not imply the opposite reaction in driver B. There are no known analytical means to solve these systems, even though they are linear, and so relatively little is known about them. This paper is a generalization of previous work that computed the global dynamics of one-dimensional sequences of identical agents (Cantos et al., Eur Phys J Special Topics 225:1115–1125, 2016) assuming periodic boundary conditions. In this paper, we push that method further, similar to Baldivieso and Veerman, IEEE Trans Control Network Syst (2021), and use an extended periodic boundary condition to to gain quantitative insights into the systems under consideration. We find that we can approximate the global dynamics of such a system by carefully analyzing the low-frequency behavior of the system with (generalized) periodic boundary conditions.
Jamming of bidisperse frictional spheres

Author(s): Srivastava, I; Roberts, SA; Clemmer, JT; Silbert, LE; Lechman, JB; Grest, GS | Abstract: By generalizing a geometric argument for frictionless spheres, a model is proposed for the jamming density φJ of mechanically stable packings of bidisperse, frictional spheres. The monodisperse, μs-dependent jamming density φJmono(μs) is the only input required in the model, where μs is the coefficient of friction. The predictions of the model are validated by robust estimates of φJ obtained from computer simulations of up to 107 particles for a wide range of μs, and size ratios up to 40:1. Although φJ varies nonmonotonically with the volume fraction of small spheres fs for all μs, its maximum value φJ,max at an optimal fmaxs are both μs dependent. The optimal fmaxs is characterized by a sharp transition in the fraction of small rattler particles.
Assessment and Clinical Utility of a Non-Next-Generation Sequencing-Based Non-Invasive Prenatal Testing Technology

Background: Rolling-circle replication (RCR) is a novel technology that has not been applied to cell-free DNA (cfDNA) testing until recently. Given the cost and simplicity advantages of this technology compared to other platforms currently used in cfDNA analysis, an assessment of RCR in clinical laboratories was performed. Here, we present the first validation study from clinical laboratories utilizing RCR technology. Methods: 831 samples from spontaneously pregnant women carrying a singleton fetus, and 25 synthetic samples, were analyzed for the fetal risk of trisomy 21 (T21), trisomy 18 (T18) and trisomy 13 (T13), by three laboratories on three continents. All the screen-positive pregnancies were provided post-test genetic counseling and confirmatory diagnostic invasive testing (e.g., amniocentesis). The screen-negative pregnancies were routinely evaluated at birth for fetal aneuploidies, using newborn examinations, and any suspected aneuploidies would have been offered diagnostic testing or confirmed with karyotyping. Results: The study found rolling-circle replication to be a highly viable technology for the clinical assessment of fetal aneuploidies, with 100% sensitivity for T21 (95% CI: 82.35-100.00%); 100.00% sensitivity for T18 (71.51-100.00%); and 100.00% sensitivity for T13 analyses (66.37-100.00%). The specificities were >99% for each trisomy (99.7% (99.01-99.97%) for T21; 99.5% (98.62-99.85%) for T18; 99.7% (99.03-99.97%) for T13), along with a first-pass no-call rate of 0.93%. Conclusions: The study showed that using a rolling-circle replication-based cfDNA system for the evaluation of the common aneuploidies would provide greater accuracy and clinical utility compared to conventional biochemical screening, and it would provide comparable results to other reported cfDNA methodologies.
Quantum Algorithms to Solve the Hidden Shift Problem for Quadratics and for Functions of Large Gowers Norm

Most quantum algorithms that give an exponential speedup over classical algorithms exploit the Fourier transform in some way. In Shor's algorithm, sampling from the quantum Fourier spectrum is used to discover periodicity of the modular exponentiation function. In a generalization of this idea, quantum Fourier sampling can be used to discover hidden subgroup structures of some functions much more efficiently than it is possible classically. Another problem for which the Fourier transform has been recruited successfully on a quantum computer is the hidden shift problem. Quantum algorithms for hidden shift problems usually have a slightly different flavor from hidden subgroup algorithms, as they use the Fourier transform to perform a correlation with a given reference function, instead of sampling from the Fourier spectrum directly. In this paper we show that hidden shifts can be extracted efficiently from Boolean functions that are quadratic forms. We also show how to identify an unknown quadratic form on n variables using a linear number of queries, in contrast to the classical case were this takes Theta(n^2) many queries to a black box. What is more, we show that our quantum algorithm is robust in the sense that it can also infer the shift if the function is close to a quadratic, where we consider a Boolean function to be close to a quadratic if it has a large Gowers U_3 norm.
Non-canonical role of Hippo tumor suppressor serine/threonine kinase 3 STK3 in prostate cancer.

Serine/threonine kinase 3 (STK3) is an essential member of the highly conserved Hippo tumor suppressor pathway that regulates Yes-associated protein 1 (YAP1) and TAZ. STK3 and its paralog STK4 initiate a phosphorylation cascade that regulates YAP1/TAZ inhibition and degradation, which is important for regulated cell growth and organ size. Deregulation of this pathway leads to hyperactivation of YAP1 in various cancers. Counter to the canonical tumor suppression role of STK3, we report that in the context of prostate cancer (PC), STK3 has a pro-tumorigenic role. Our investigation started with the observation that STK3, but not STK4, is frequently amplified in PC. Additionally, high STK3 expression is associated with decreased overall survival and positively correlates with androgen receptor (AR) activity in metastatic castrate-resistant PC. XMU-MP-1, an STK3/4 inhibitor, slowed cell proliferation, spheroid growth, and Matrigel invasion in multiple models. Genetic depletion of STK3 decreased proliferation in several PC cell lines. In a syngeneic allograft model, STK3 loss slowed tumor growth kinetics in vivo, and biochemical analysis suggests a mitotic growth arrest phenotype. To further probe the role of STK3 in PC, we identified and validated a new set of selective STK3 inhibitors, with enhanced kinase selectivity relative to XMU-MP-1, that inhibited tumor spheroid growth and invasion. Consistent with the canonical role, inhibition of STK3 induced cardiomyocyte growth and had chemoprotective effects. Our results indicate that STK3 has a non-canonical role in PC progression and that inhibition of STK3 may have a therapeutic potential for PC that merits further investigation.
Overcomplete Representations Against Adversarial Videos

Adversarial robustness of deep neural networks is an extensively studied problem in the literature and various methods have been proposed to defend against adversarial images. However, only a handful of defense methods have been developed for defending against attacked videos. In this paper, we propose a novel Over-and-Under complete restoration network for Defending against adversarial videos (OUDefend). Most restoration networks adopt an encoder-decoder architecture that first shrinks spatial dimension then expands it back. This approach learns undercomplete representations, which have large receptive fields to collect global information but overlooks local details. On the other hand, overcomplete representations have opposite properties. Hence, OUDefend is designed to balance local and global features by learning those two representations. We attach OUDefend to target video recognition models as a feature restoration block and train the entire network end-to-end. Experimental results show that the defenses focusing on images may be ineffective to videos, while OUDefend enhances robustness against different types of adversarial videos, ranging from additive attacks, multiplicative attacks to physically realizable attacks. Code: this https URL
Low/medium density biomass, coastal and ocean carbon: a carbon cycle mission

Abstract As part of the Global Carbon Cycle research effort, an agency-wide planning initiative was organized between October 2000 and June 2001 by the NASA Goddard Space Flight Center at the behest of the Associate Administrator for Earth Science. The goal was to define future research and technology development activities needed for implementing a cohesive scientific observation plan. A timeline for development of missions necessary to acquire the selected new measurements was laid out, and included missions for low–medium density terrestrial biomass/coastal ocean, and global ocean carbon. This paper will begin with the scientific justification and measurement requirements for these specific activities, lightly touch on the options for having separate low Earth orbiting missions, and follow-up in more detail with a combined implementation study centered on a hyperspectral imager at geosynchronous altitudes, highlighting both its merits and challenges.
New Constraints on Pluto's Sputnik Planitia Ice Sheet from a Coupled Reorientation-Climate Model

We present a coupled reorientation and climate model, to understand how true polar wander (TPW) and atmospheric condensation worked together to create the Sputnik Planitia (SP) ice sheet and reorient it to its present-day location on Pluto. SP is located at 18 N, 178 E, very close to the anti-Charon point, and it has been previously shown that this location can be explained by TPW reorientation of an impact basin as it fills with N2 ice. We readdress that hypothesis while including a more accurate treatment of Pluto's climate and orbital obliquity cycle. Our model again finds that TPW is a viable mechanism for the formation and present-day location of SP. We find that the initial impact basin could have been located north of the present-day location, at latitudes between 35 N and 50 N. The empty basin is constrained to be 2.5 -- 3 km deep, with enough N2 available to form at most a 1 -- 2 km thick ice sheet. Larger N2 inventories reorient too close to the anti-Charon point. After reaching the final location, the ice sheet undergoes short periods of sublimation and re-condensation on the order of ten meters of ice, due Pluto's variable obliquity cycle, which drives short periods of reorientation of a few km. The obliquity cycle also has a role in the onset of infilling; some initial basin locations are only able to begin accumulating N2 ice at certain points during the obliquity cycle. We also explore the sensitivity of the coupled model to albedo, initial obliquity, and Pluto's orbit.
LabView Interface for School-Network DAQ Card

As described elsewhere at this conference, a low-cost DAQ card has been developed for school-network cosmic ray detector projects, providing digitized data from photomultiplier tubes via a standard serial interface. To facilitate analysis of these data and to provide students with a starting point for custom readout systems, a model interface has been developed using the National Instruments LabVIEW ® system. This user-friendly interface allows one to initialize the trigger coincidence conditions for data-taking runs and to monitor incoming or pre-recorded data sets with updating singles- and coincidence-rate plots and other user-selectable histograms.
The effects of the OPT visa extension rule on STEM business programs in the U.S.

I investigate the effects of STEM Optional Practical Training (OPT) visa extension rule on Science, Technology, Engineering, and Mathematics (STEM) programme offerings and student degree attainment...
Exposure, Experience, and Expertise: Why Personal Histories Matter in Economics

Personal experiences of economic outcomes, from global financial crises to individual-level job losses, can shape individual beliefs, risk attitudes, and choices for years to come. A growing literature on experience effects shows that individuals act as if past outcomes that they experienced were overly likely to occur again, even if they are fully informed about the actual likelihood. This reaction to past experiences is long-lasting though it decays over time as individuals accumulate new experiences. Modern brain science helps understand these processes. Evidence on neuroplasticity reveals that personal experiences and learning alter the strength of neural connections and fine-tune the brain structure to those past experiences ("use-dependent brain"). I show that experience effects help understand belief formation and decision-making in a wide area of economic applications, including inflation, home purchases, mortgage choices, and consumption expenditures. I argue that experience-based learning is broadly applicable to economic decision-making and discuss topics for future research in education, health, race, and gender economics.
Rational points on conic bundles over elliptic curves

We study rational points on conic bundles over elliptic curves with positive rank over a number field. We show that the etale Brauer–Manin obstruction is insufficient to explain failures of the Hasse principle for such varieties. We then further consider properties of the distribution of the set of rational points with respect to its image in the rational points of the elliptic curve. In the process, we prove results on a local-to-global principle for torsion points on elliptic curves over $${{{\mathbb {Q}}}}$$ .
DistGNN: scalable distributed training for large-scale graph neural networks

Full-batch training on Graph Neural Networks (GNN) to learn the structure of large graphs is a critical problem that needs to scale to hundreds of compute nodes to be feasible. It is challenging due to large memory capacity and bandwidth requirements on a single compute node and high communication volumes across multiple nodes. In this paper, we present DistGNN that optimizes the well-known Deep Graph Library (DGL) for full-batch training on CPU clusters via an efficient shared memory implementation, communication reduction using a minimum vertex-cut graph partitioning algorithm and communication avoidance using a family of delayed-update algorithms. Our results on four common GNN benchmark datasets: Reddit, OGB-Products, OGB-Papers and Proteins, show up to 3.7× speed-up using a single CPU socket and up to 97× speed-up using 128 CPU sockets, respectively, over baseline DGL implementations running on a single CPU socket.
Cancellation Properties of Graded and Nonunital Rings Graded Clean and Graded Exchange Leavitt Path Algebras

Various authors have been generalizing some unital ring properties to nonunital rings. We consider properties related to cancellation of modules (being unit-regular, having stable range one, being directly finite, exchange, or clean) and their "local" versions. We explore their relationships and extend the defined concepts to graded rings. With graded clean and graded exchange rings suitably defined, we study how these properties behave under the formation of graded matrix rings. We exhibit properties of a graph $E$ which are equivalent to the unital Leavitt path algebra $L_K(E)$ being graded clean. We also exhibit some graph properties which are necessary and some which are sufficient for $L_K(E)$ to be graded exchange.
Fractional elliptic equations in nondivergence form: definition, applications and Harnack inequality

Abstract We define the fractional powers L s = ( − a i j ( x ) ∂ i j ) s , 0 s 1 , of nondivergence form elliptic operators L = − a i j ( x ) ∂ i j in bounded domains Ω ⊂ R n , under minimal regularity assumptions on the coefficients a i j ( x ) and on the boundary ∂Ω. We show that these fractional operators appear in several applications such as fractional Monge–Ampere equations, elasticity, and finance. The solution u to the nonlocal Poisson problem { ( − a i j ( x ) ∂ i j ) s u = f in Ω u = 0 on ∂ Ω is characterized by a local degenerate/singular extension problem. We develop the method of sliding paraboloids in the Monge–Ampere geometry and prove the interior Harnack inequality and Holder estimates for solutions to the extension problem when the coefficients a i j ( x ) are bounded, measurable functions. This in turn implies the interior Harnack inequality and Holder estimates for solutions u to the fractional problem.
On Adversarial Vulnerability of PHM algorithms: An Initial Study

In almost all PHM applications, driving highest possible performance (prediction accuracy and robustness) of PHM models (fault detection, fault diagnosis and prognostics) has been the top development priority, since PHM models’ performance directly impacts how much business value the PHM models can bring. However, recent research work in other domains, e.g., computer vision (CV), has shown that machine learning (ML) models, especially deep learning models, are vulnerable to adversarial attacks; that is, small deliberately-designed perturbations to the original samples can cause the model to make false predictions with high confidence. In fact, adversarial machine learning (AML) targeting security of ML algorithms against adversaries, has become an emerging ML topic and has attracted tremendous research attention in CV and NLP. Yet, in the PHM community, not much attention has been paid to adversarial vulnerability or security of PHM models. We contend that the economic impact of adversarial attacks to a PHM model might be even bigger than that to hard perceptual problems and thus securing PHM models from adversarial attacks is as important as the PHM models themselves. Also, PHM models, since the data used by the models are primarily time-series sensor measurements, have their own unique characteristics and deserve special attention in securing them. In this paper we attempt to explore the adversarial vulnerability of PHM models by conducting an initial case study. More specifically, we consider several unique characteristics associated with streaming time-series sensor measurements data in developing attack strategies for attacking PHM models. We hope our initial study here can shed some light on and stimulate more research interests in the area of PHM models’ security.
Singularity of discrete random matrices

Let $$\xi $$ be a non-constant real-valued random variable with finite support and let $$M_{n}(\xi )$$ denote an $$n\times n$$ random matrix with entries that are independent copies of $$\xi $$ . For $$\xi $$ which is not uniform on its support, we show that $$\begin{aligned} {\mathbb {P}}[M_{n}(\xi )\text { is singular}]&= {\mathbb {P}}[\text {zero row or column}] \\ {}&\quad +(1+o_n(1)){\mathbb {P}}[\text {two equal (up to sign) rows or columns}], \end{aligned}$$ thereby confirming a folklore conjecture. As special cases, we obtain: For $$\xi $$ which is uniform on its support:
CrisisFlow: Multimodal Representation Learning Workflow for Crisis Computing

An increasing number of people use social media (SM) platforms like Twitter and Instagram to report critical emergencies or disaster events. Multimodal data shared on these platforms often contain useful information about the scale of the event, victims, and infrastructure damage. The data can provide local authorities and humanitarian organizations with a big-picture understanding of the emergency (situational awareness). Moreover, it can be used to effectively and timely plan relief responses. In our project, we aim to address the challenge of finding relevant information among the vast amount of published SM posts. Specifically, we use deep learning algorithms to produce embeddings that encode the informativeness of multimodal SM data in the context of disaster events. Our method improves the state-of-the-art performance on the informative vs. non-informative classification task for the CrisisMMD dataset. To ensure the reliability and scalability of our solution in real-world scenarios, we implement the resulting crisis computing workflow in the Pegasus Workflow Management System (WMS).
Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman Operators

In temporal difference (TD) learning, off-policy sampling is known to be more practical than on-policy sampling, and by decoupling learning from data collection, it enables data reuse. It is known that policy evaluation (including multi-step off-policy importance sampling) has the interpretation of solving a generalized Bellman equation. In this paper, we derive finite-sample bounds for any general off-policy TD-like stochastic approximation algorithm that solves for the fixed-point of this generalized Bellman operator. Our key step is to show that the generalized Bellman operator is simultaneously a contraction mapping with respect to a weighted $\ell_p$-norm for each $p$ in $[1,\infty)$, with a common contraction factor. Off-policy TD-learning is known to suffer from high variance due to the product of importance sampling ratios. A number of algorithms (e.g. $Q^\pi(\lambda)$, Tree-Backup$(\lambda)$, Retrace$(\lambda)$, and $Q$-trace) have been proposed in the literature to address this issue. Our results immediately imply finite-sample bounds of these algorithms. In particular, we provide first-known finite-sample guarantees for $Q^\pi(\lambda)$, Tree-Backup$(\lambda)$, and Retrace$(\lambda)$, and improve the best known bounds of $Q$-trace in [19]. Moreover, we show the bias-variance trade-offs in each of these algorithms.
Optimal delocalization for generalized Wigner matrices

Abstract We study the eigenvectors of generalized Wigner matrices with subexponential entries and prove that they delocalize at the optimal rate with overwhelming probability. We also prove high probability delocalization bounds with sharp constants. Our proof uses an analysis of the eigenvector moment flow introduced by Bourgade and Yau (2017) [18] to bound logarithmic moments of eigenvector entries for random matrices with small Gaussian components. We then extend this control to all generalized Wigner matrices by comparison arguments based on a framework of regularized eigenvectors, level repulsion, and the observable employed by Landon, Lopatto, and Marcinek (2018) [45] to compare extremal eigenvalue statistics. Additionally, we prove level repulsion and eigenvalue overcrowding estimates for the entire spectrum, which may be of independent interest.
Representation Learning for Event-based Visuomotor Policies

Event-based cameras are dynamic vision sensors that can provide asynchronous measurements of changes in per-pixel brightness at a microsecond level. This makes them significantly faster than conventional frame-based cameras, and an appealing choice for high-speed navigation. While an interesting sensor modality, this asynchronous data poses a challenge for common machine learning techniques. In this paper, we present an event variational autoencoder for unsupervised representation learning from asynchronous event camera data. We show that it is feasible to learn compact representations from spatiotemporal event data to encode the context. Furthermore, we show that such pretrained representations can be beneficial for navigation, allowing for usage in reinforcement learning instead of end-to-end reward driven perception. We validate this framework of learning visuomotor policies by applying it to an obstacle avoidance scenario in simulation. We show that representations learnt from event data enable training fast control policies that can adapt to different control capacities, and demonstrate a higher degree of robustness than end-to-end learning from event images.
Reconfiguration Graph Grammar for Massively Parallel, Fault Tolerant Computers

Reconfiguration for fault tolerance is the process of excluding faulty processors from an array of interconnected processors and including spare processors to take their place. Reconfiguration Graph Grammar (RGG) is introduced as a model supporting the design and analysis of these reconfiguration algorithms. A formal description is given, as well as several theorems, with proofs, concerning the properties of RGG that make it well suited for modeling reconfiguration. An example RGG-based reconfiguration algorithm is described and demonstrated.
Testing of synchronization conditions for distributed real-time applications

A set of synchronization relations between distributed nonatomic events was recently proposed to provide real-time applications with a fine level of discrimination in the specification of causality relations and synchronization conditions. For a pair of distributed nonatomic events X and Y, the evaluation of the synchronization relations requires | NX| x | NY | integer comparisons, where | Nx | and | Ny |, respectively, are the number of nodes on which the two nonatomic events X and Y occur. In this paper, we show that this polynomial complexity of evaluation can by simplified using properties of partial orders to a linear complexity. Specifically, we show that most relations can be evaluated in min(|NX |, |NY|) integer comparisons, some in |NX| integer comparisons, and the others in |NY| integer comparisons. These linear time evaluation conditions enable the real-time applications to detect the relations efficiently.
Using the theory of constraints' production application in a semiconductor fab with a reentrant bottleneck

In early 1995, Harris Semiconductor's Findlay plant embarked on the process of developing the theory of constraints (TOC) drum-buffer-rope (DBR) production application in our wafer fabs, using a reentrant bottleneck, in which the product passes through the bottleneck multiple times. We formed a lead team to customize the generic production application to our unique situation and learned much about how to apply each of the components of DBR to a reentrant bottleneck situation. For example, to determine how many wafers to start each week, we determine the total bottleneck moves for the previous week, divide by the average number of bottleneck repetitions per wafer, and adjust for yield. For the past three years, the plant has been using this system, and it has evolved greatly. During that time, the bottleneck has changed several times, though we chose to maintain the formal bottleneck throughout this time. In addition, we have gone through several periods where the constraint shifted to the market, in which capacity outstripped demand. Using five focusing steps, we doubled bottleneck capacity in the focus fab over this time. Activities around the improvement included decoupling the bottleneck operation from a nonbottleneck operation, modifying operator shifts and work rules, improving capability at the bottleneck through various improvement projects, and using critical ratio to schedule all nonbottleneck operations.
Pruning support vectors for imbalanced data classification

In many practical applications, learning from imbalanced data poses a significant challenge that is increasingly faced by the machine learning community. The class imbalance problem raises issues that are either nonexistent or less severe compared to balanced class cases. This paper presents a new method for imbalanced data classification. The proposed method is based on support vector machine classifiers and backward pruning technique. The experimental results obtained on two data sets demonstrate the effectiveness of the new algorithm.
Woodin cardinals, Shelah cardinals, and the Mitchell-Steel core model

Theorem 4 is a characterization of Woodin cardinals in terms of Skolem hulls and Mostowski collapses. We define weakly hyper-Woodin cardinals and hyper-Woodin cardinals. Theorem 5 is a covering theorem for the Mitchell-Steel core model, which is constructed using total background extenders. Roughly, Theorem 5 states that this core model correctly computes successors of hyper-Woodin cardinals. within the large cardinal hierarchy, in increasing order we have: measurable Woodin, weakly hyper-Woodin, Shelah, hyper-Woodin, and superstrong cardinals. (The comparison of Shelah versus hyper-Woodin is due to James Cummings.)
Time‐lapse pre‐stack seismic data registration and inversion for CO2 sequestration study at Cranfield

Pre-stack seismic data are indicative of subsurface elastic properties within the amplitude versus offset characteristic and can be used to detect elastic rock property changes caused by CO2 injection. We perform time-lapse pre-stack 3-D seismic data analysis for monitoring CO2 sequestration at Cranfield. The time-lapse amplitude differences of Cranfield datasets are found entangled with time-shifts. To disentangle these two characters, we apply a local-correlation-based warping method to register the time-lapse pre-stack datasets, which can effectively separate the time-shift from the time-lapse seismic amplitude difference without changing the original amplitudes. We demonstrate the effectiveness of our registration method by evaluating the inverted elastic properties. These inverted time-lapse elastic properties can be reliably used for monitoring CO2 plumes.
What Microbes Are Lurking in Your Investigation House? Identification of Unknown Microorganisms Using a PCR-Based Lab Experiment

In an effort to update the microbiology teaching-lab curriculum by making lab experiments more current, we designed a microbiology lab experiment that used the polymerase chain reaction to identify microbes growing in students' homes. This experiment successfully engaged students, reinforced theoretical information presented in lectures, provided our students with valuable and current molecular technique exposure, and made molecular microbiology personally meaningful and exciting.
Delta, theta, and alpha event-related oscillations in alcoholics during Go/NoGo task: Neurocognitive deficits in execution, inhibition, and attention processing

Higher impulsivity observed in alcoholics is thought to be due to neurocognitive functional deficits involving impaired inhibition in several brain regions and/or neuronal circuits. Event-related oscillations (EROs) offer time-frequency measure of brain rhythms during perceptual and cognitive processing, which provide a detailed view of neuroelectric oscillatory responses to external/internal events. The present study examines evoked power (temporally locked to events) of oscillatory brain signals in alcoholics during an equal probability Go/NoGo task, assessing their functional relevance in execution and inhibition of a motor response. The current study hypothesized that increases in the power of slow frequency bands and their topographical distribution is associated with tasks that have increased cognitive demands, such as the execution and inhibition of a motor response. Therefore, it is hypothesized that alcoholics would show lower spectral power in their topographical densities compared to controls. The sample consisted of 20 right-handed abstinent alcoholic males and 20 age and gender-matched healthy controls. Evoked delta (1.0-3.5Hz; 200-600ms), theta (4.0-7.5Hz; 200-400ms), slow alpha (8.0-9.5Hz; 200-300ms), and fast alpha (10.0-12.5Hz; 100-200ms) ERO power were compared across group and task conditions. Compared to controls, alcoholics had higher impulsiveness scores on the Barrett Impulsiveness Scale (BIS-11) and made more errors on Go trials. Alcoholics showed significantly lower evoked delta, theta, and slow alpha power compared to controls for both Go and NoGo task conditions, and lower evoked fast alpha power compared to controls for only the NoGo condition. The results confirm previous findings and are suggestive of neurocognitive deficits while executing and suppressing a motor response. Based on findings in the alpha frequency ranges, it is further suggested that the inhibitory processing impairments in alcoholics may arise from inadequate early attentional processing with respect to the stimulus related aspects/semantic memory processes, which may be reflected in lower posterio-temporal evoked fast alpha power. It can thus be concluded that alcoholics show neurocognitive deficits in both execution and suppression of a motor response and inadequate early attentional processing with respect to the semantic memory/stimulus related aspects while suppressing a motor response.
Potential for inferring freshwater lake ice thickness by GPS interferometric reflectometry

Recently, it has been reported that the refracted, reflected and scattered signals of global navigation satellite systems (GNSS) have been successfully used to remotely sense the Earth's surface and atmosphere. For example, it has been reported that reflected global positioning system (GPS) signals can provide useful information about the land-surface composition such as snow depth. From recent snow depth studies, this promising new technique has been given the name GPS interferometric reflectometry (GPS-IR). This method is basically an L-band ground-based interferometer. Its basic mechanism is the interference between the direct (line-of-sight) signal and the multipath signals, reflected from near-ground surfaces such as snow, ice, water, etc. Here, we explore the possibility of estimating freshwater lake ice thickness. In particular, a GPS receiver is located above a frozen lake. With this setup, the received power variations with respect to the changing satellite elevation angle are calculated and measured. A case study shows potential for inferring lake ice thickness by fitting the theory to the measurements using a nonlinear least squares fitting algorithm.
Explicit formulae for the continued fraction convergents of

The formulae for the convergents of continued fractions are always given recursively rather than in explicit form. This article derives explicit formulae for the convergents of the continued fraction expansions for square roots.
Spontaneous rupture of the oesophagus and massive pneumoperitoneum

A case of spontaneous rupture of the abdominal oesophagus is described. The presentation, after an interval of 4 weeks from the onset of symptons, was unusual with massive pneumoperitoneum without peritonitis.