cssi/SciSciGPT-SciSciNet huggingface dataset:
Deep learning for insider threat detection: Review, challenges and opportunities

Abstract Insider threats, as one type of the most challenging threats in cyberspace, usually cause significant loss to organizations. While the problem of insider threat detection has been studied for a long time in both security and data mining communities, the traditional machine learning based detection approaches, which heavily rely on feature engineering, are hard to accurately capture the behavior difference between insiders and normal users due to various challenges related to the characteristics of underlying data, such as high-dimensionality, complexity, heterogeneity, sparsity, lack of labeled insider threats, and the subtle and adaptive nature of insider threats. Advanced deep learning techniques provide a new paradigm to learn end-to-end models from complex data. In this brief survey, we first introduce commonly-used datasets for insider threat detection and review the recent literature about deep learning for such research. The existing studies show that compared with traditional machine learning algorithms, deep learning models can improve the performance of insider threat detection. However, applying deep learning to further advance the insider threat detection task still faces several limitations, such as lack of labeled data, adaptive attacks. We discuss such challenges and suggest future research directions that have the potential to address challenges and further boost the performance of deep learning for insider threat detection.
Exact lower bound on an ''exactly one'' probability

We obtain the exact lower bound on the probability of the occurrence of exactly one of n random events each of probability p.
Gait Parameters Measured from Wearable Sensors Reliably Detect Freezing of Gait in a Stepping in Place Task

Freezing of gait (FOG), a debilitating symptom of Parkinson’s disease (PD), can be safely studied using the stepping in place (SIP) task. However, clinical, visual identification of FOG during SIP is subjective and time consuming, and automatic FOG detection during SIP currently requires measuring the center of pressure on dual force plates. This study examines whether FOG elicited during SIP in 10 individuals with PD could be reliably detected using kinematic data measured from wearable inertial measurement unit sensors (IMUs). A general, logistic regression model (area under the curve = 0.81) determined that three gait parameters together were overall the most robust predictors of FOG during SIP: arrhythmicity, swing time coefficient of variation, and swing angular range. Participant-specific models revealed varying sets of gait parameters that best predicted FOG for each participant, highlighting variable FOG behaviors, and demonstrated equal or better performance for 6 out of the 10 participants, suggesting the opportunity for model personalization. The results of this study demonstrated that gait parameters measured from wearable IMUs reliably detected FOG during SIP, and the general and participant-specific gait parameters allude to variable FOG behaviors that could inform more personalized approaches for treatment of FOG and gait impairment in PD.
Characterizing the Chemistry of Planetary Materials Around White Dwarf Stars

Planetary systems that orbit white dwarf stars can be studied via spectroscopic observations of the stars themselves. Numerous white dwarfs are seen to have accreted mostly rocky minor planets, the remnants of which are present in the stellar photospheres. The elemental abundances in the photospheres unveil the bulk compositions of the accreted parent bodies with a precision far greater than can be attained with any other technique currently available to astronomers. The most significant discovery, overall, is that rocky extrasolar planets have bulk elemental compositions similar to those of Earth and other rocky objects in our solar system. The white dwarf studies reveal that many extrasolar minor planets (asteroids) are differentiated, possessing analogs of terrestrial crust, mantle and core; this finding has important implications for the origin of our own solar system.
On genericity of shadowing in one dimension

We show that shadowing is a generic property among continuous maps and surjections on a large class of locally connected one-dimensional continua.
The sex-specific VC neurons are mechanically activated motor neurons that facilitate serotonin-induced egg laying in C. elegans

Successful execution of behavior requires coordinated activity and communication between multiple cell types. Studies using the relatively simple neural circuits of invertebrates have helped to uncover how conserved molecular and cellular signaling events shape animal behavior. To understand the mechanisms underlying neural circuit activity and behavior, we have been studying a simple circuit that drives egg-laying behavior in the nematode worm C. elegans. Here we show that the sex-specific, Ventral C (VC) motor neurons are important for vulval muscle contractility and egg laying in response to serotonin. Ca2+ imaging experiments show the VCs are active during times of vulval muscle contraction and vulval opening, and optogenetic stimulation of the VCs promotes vulval muscle Ca2+ activity. Blocking VC neurotransmission inhibits egg laying in response to serotonin and increases the failure rate of egg-laying attempts, indicating that VC signaling facilitates full vulval muscle contraction and opening of the vulva for efficient egg laying. We also find the VCs are mechanically activated in response to vulval opening. Optogenetic stimulation of the vulval muscles is sufficient to drive VC Ca2+ activity and requires muscle contractility, showing the presynaptic VCs and the postsynaptic vulval muscles can mutually excite each other. Together, our results demonstrate that the VC neurons facilitate efficient execution of egg-laying behavior by coordinating postsynaptic muscle contractility in response to serotonin and mechanosensory feedback. SIGNIFICANCE STATEMENT: Many animal motor behaviors are modulated by the neurotransmitters serotonin and acetylcholine. Such motor circuits also respond to mechanosensory feedback, but how neurotransmitters and mechanoreceptors work together to coordinate behavior is not well understood. We address these questions using the egg-laying circuit in C. elegans where we can manipulate presynaptic neuron and postsynaptic muscle activity in behaving animals while recording circuit responses through Ca2+ imaging. We find that the cholinergic VC motoneurons are important for proper vulval muscle contractility and egg laying in response to serotonin. Muscle contraction also activates the VCs, forming a positive feedback loop that promotes full contraction for egg release. In all, mechanosensory feedback provides a parallel form of modulation that shapes circuit responses to neurotransmitters.
Family history of depression is associated with alterations in task-dependent connectivity between the cerebellum and ventromedial prefrontal cortex.

Background A family history of major depressive disorder (MDD) increases the likelihood of a future depressive episode, which itself poses a significant risk for disruptions in reward processing and social cognition. However, it is unclear whether a family history of MDD is associated with alterations in the neural circuitry underlying reward processing and social cognition. Methods We subdivided 279 participants from the Human Connectome Project into three groups: 71 with a lifetime history of MDD, 103 with a family history (FH) of MDD, and 105 healthy controls (HCs). We then evaluated task-based functional magnetic resonance imaging data on a social cognition and a reward processing task and found a region of the ventromedial prefrontal cortex (vmPFC) that responded to both tasks, independent of the group. To investigate whether the vmPFC shows alterations in functional connectivity between groups, we conducted psychophysiological interaction analyses using the vmPFC as a seed region. Results We found that FH (relative to HC) was associated with increased sadness scores, and MDD (relative to both FH and HC) was associated with increased sadness and MDD symptoms. Additionally, the FH group had increased vmPFC functional connectivity within the nucleus accumbens, left dorsolateral PFC, and subregions of the cerebellum relative to HC during the social cognition task. Conclusions These findings suggest that aberrant neural mechanisms among those with a familial risk of MDD may underlie vulnerability to altered social cognition.
Exploring Hubble Constant Data in an Introductory Course

Popular accounts of exciting discoveries often draw students to physics and astronomy, but at the introductory level it is challenging to connect with these in a meaningful way. The use of real astronomical data in the classroom can help bridge this gap and build valuable quantitative and scientific reasoning skills. This paper presents a strategy for studying Hubble’s law and the accelerating expansion of the universe using actual data. Along with understanding the physical concepts, an explicit goal is to develop skills for analyzing data in terms of a model.
Econometric models of duration data in entrepreneurship with an application to start-ups' time-to-funding by venture capitalists (VCs)

Because time is a key determinant of entrepreneurial decision making, time-to-event models are ubiquitous in entrepreneurship. Widespread econometric misconception, however, may cause complicated b...
A genetic risk score for glioblastoma multiforme based on copy number variations.

Abstract Glioblastoma multiforme is the most common form of brain cancer. Several lines of evidence suggest that glioblastoma multiforme has a genetic basis. A genetic test that could identify people who are at high risk of developing glioblastoma multiforme could improve our understanding of this form of brain cancer. Using the Cancer Genome Atlas (TCGA) dataset, we found common germ line DNA copy number variations in the TCGA population. We tested whether different sets of these germ line DNA copy number variations could effectively distinguish patients with glioblastoma multiforme from others in the TCGA dataset. We used a gradient boosting machine, a machine learning classification algorithm, to classify TCGA patients solely based on a set of germline DNA copy number variations. We found that this machine learning algorithm could classify TCGA glioblastoma multiforme patients from the other TCGA patients with an area under the curve (AUC) of the receiver operating characteristic curve (AUC=0.875). Grouped into quintiles, the highest ranked quintile by the machine learning algorithm had an odds ratio of 3.78 (95% CI 3.25–4.40) higher than the average odds ratio and about 40 (95% CI 20–70) times higher than the lowest quintile. The identification of an effective germ line genetic test to stratify risk of developing glioblastoma multiforme should lead to a better understanding of how this cancer forms. This result might ultimately lead to better treatments of glioblastoma multiforme.
All maximal independent sets and dynamic dominance for sparse graphs

We describe algorithms, based on Avis and Fukuda's reverse search paradigm, for listing all maximal independent sets in a sparse graph in polynomial time and delay per output. For bounded degree graphs, our algorithms take constant time per set generated; for minor-closed graph families, the time is O(n) per set, and for more general sparse graph families we achieve subquadratic time per set. We also describe new data structures for maintaining a dynamic vertex set S in a sparse or minor-closed graph family, and querying the number of vertices not dominated by S; for minor-closed graph families the time per update is constant, while it is sublinear for any sparse graph family. We can also maintain a dynamic vertex set in an arbitrary m-edge graph and test the independence of the maintained set in time O(√m) per update. We use the domination data structures as part of our enumeration algorithms.
Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination

The increasing impact of algorithmic decisions on people’s lives compels us to scrutinize their fairness and, in particular, the disparate impacts that ostensibly color-blind algorithms can have on...
Instruction and Research at the Daniel and Florence Guggenheim Jet Propulsion Center

In "America Fledges Wings" R. M. Cleveland [1] stated: "Despite the importance of the role played by the Daniel Guggenheim Fund for the promotion of aeronautics as a herald, as an awakener, as a quickening spark in the manifold fields of practical aviation, its most important and probably most lasting contribution lay in its implementation and its creation of centers of research." These centers of research are the well-known great schools of aeronautical engineering at the New York University, the Stanford University, the University of Michigan, the Massachusetts Institute of Technology, the California Institute of Technology, the University of Washington, and the Georgia School of Technology. It is a fact that a great majority of practicing aeronautical engineers today are either wholly educated in one of these centers or have had contact with one of these centers. Moreover, the strong influence of the Guggenheim Fund is not limited to this phase of aeronautical engineering. These centers of research contributed to a large extent to the fundamental knowledge of aeronautical science which forms the scientific basis of aeronautical engineering. Today we see an even more broadened effect exerted by the Guggenheim schools as men originally educated in these Guggenheim research centers establish new research laboratories and new schools of aeronautics in universities all over the world.
Leveraging user access patterns and advanced cyberinfrastructure to accelerate data delivery from shared-use scientific observatories

Abstract With the growing number and increasing availability of shared-use instruments and observatories, observational data is becoming an essential part of application workflows and contributor to scientific discoveries in a range of disciplines. However, the corresponding growth in the number of users accessing these facilities coupled with the expansion in the scale and variety of the data, is making it challenging for these facilities to ensure their data can be accessed, integrated, and analyzed in a timely manner, and is resulting significant demands on their cyberinfrastructure (CI). In this paper, we present the design of a push-based data delivery framework that leverages emerging in-network capabilities, along with data pre-fetching techniques based on a hybrid data management model. Specifically, we analyze data access traces for two large-scale observatories, Ocean Observatories Initiative (OOI) and Geodetic Facility for the Advancement of Geoscience (GAGE), to identify typical user access patterns and to develop a model that can be used for data pre-fetching. Furthermore, we evaluate our data pre-fetching model and the proposed framework using a simulation of the Virtual Data Collaboratory (VDC) platform that provides in-network data staging and processing capabilities. The results demonstrate that the ability of the framework to significantly improve data delivery performance and reduce network traffic at the observatories’ facilities.
Design, Function and Structure of a Monomeric CLC Transporter

The CLC family of Cl- channels and Cl-/H+ antiporters are homodimeric in structure with each subunit containing a distinct transport pathway. Yet, the role of this conserved dimeric architecture in relation to protein function is not well understood. While certain CLC channels demonstrate dimer-dependent gating through cytosolic domains, the near-normal function of a bacterial CLC transporter strait-jacketed by covalent crosslinks across the dimer interface argues that the transport cycle resides within each subunit and does not require rigid-body rearrangements between subunits. A prediction that follows is that it should be possible to construct a monomeric form of a CLC protein while preserving structural and functional properties. To investigate this, we designed a stable monomeric variant of the E. coli CLC transporter, CLC-ec1, by introducing two tryptophan mutations, I201W and I422W, at the dimer interface. The existence of monomer was confirmed by gel filtration and glutaraldehyde cross-linking in both detergent micelles and PC/PG liposomes. The monomer is functional, exhibiting H+ coupled Cl- transport at rates comparable to the wild-type dimer and with 2:1 stoichiometry. The structure of the monomer was solved to 3.1 A resolution showing a crystal packing arrangement that exposes the previously buried dimer interface. The backbone atoms of this new structure aligns with the original CLC-ec1 crystal structure with 0.4 A RMSD, showing that the intrinsic subunit structure is not affected by these mutations or separation from the dimer state. These results demonstrate that the CLC subunit alone is the basic functional unit for transport and that cross-subunit interaction is not required for Cl-/H+ exchange in CLC transporters.
Measuring the electron temperature and identifying plasma detachment using machine learning and spectroscopy.

A machine learning approach has been implemented to measure the electron temperature directly from the emission spectra of a tokamak plasma. This approach utilized a neural network (NN) trained on a dataset of 1865 time slices from operation of the DIII-D tokamak using extreme ultraviolet/vacuum ultraviolet emission spectroscopy matched with high-accuracy divertor Thomson scattering measurements of the electron temperature, Te. This NN is shown to be particularly good at predicting Te at low temperatures (Te < 10 eV) where the NN demonstrated a mean average error of less than 1 eV. Trained to detect plasma detachment in the tokamak divertor, a NN classifier was able to correctly identify detached states (Te < 5 eV) with a 99% accuracy (an F1 score of 0.96) at an acquisition rate 10× faster than the Thomson scattering measurement. The performance of the model is understood by examining a set of 4800 theoretical spectra generated using collisional radiative modeling that was also used to predict the performance of a low-cost spectrometer viewing nitrogen emission in the visible wavelengths. These results provide a proof-of-principle that low-cost spectrometers leveraged with machine learning can be used to boost the performance of more expensive diagnostics on fusion devices and be used independently as a fast and accurate Te measurement and detachment classifier.
Sequential prediction under log-loss with side information

The problem of online prediction with sequential side information under logarithmic loss is studied, and general upper and lower bounds on the minimax regret incurred by the predictor is established. The upper bounds on the minimax regret are obtained by providing and analyzing a probability assignment inspired by mixture probability assignments in universal compression, and the lower bounds are obtained by way of a redundancy-capacity theorem. The tight characterization of the regret is provided in some special settings.
Second order rectifiability of varifolds of bounded mean curvature

We prove that the support of an m dimensional rectifiable varifold with a uniform lower bound on the density and bounded generalized mean curvature can be covered $$ {\mathscr {H}}^{m} $$ almost everywhere by a countable union of m dimensional submanifolds of class $$ {\mathcal {C}}^{2} $$ . The $$ {\mathcal {C}}^{2} $$ -regularity of the submanifolds is optimal.
High intensity muscle stimulation activates a systemic Nrf2-mediated redox stress response.

Abstract High intensity exercise is a popular mode of exercise to elicit similar or greater adaptive responses compared to traditional moderate intensity continuous exercise. However, the molecular mechanisms underlying these adaptive responses are still unclear. The purpose of this pilot study was to compare high and low intensity contractile stimulus on the Nrf2-mediated redox stress response in mouse skeletal muscle. An intra-animal design was used to control for variations in individual responses to muscle stimulation by comparing a stimulated limb (STIM) to the contralateral unstimulated control limb (CON). High Intensity (HI – 100Hz), Low Intensity (LI – 50Hz), and Naive Control (NC – Mock stimulation vs CON) groups were used to compare these effects on Nrf2-ARE binding, Keap1 protein, and downstream gene and protein expression of Nrf2 target genes. Muscle stimulation significantly increased Nrf2-ARE binding in LI-STIM compared to LI-CON (p = 0.0098), while Nrf2-ARE binding was elevated in both HI-CON and HI-STIM compared to NC (p = 0.0007). The Nrf2-ARE results were mirrored in the downregulation of Keap1, where Keap1 expression in HI-CON and HI-STIM were both significantly lower than NC (p = 0.008) and decreased in LI-STIM compared to LI-CON (p = 0.015). In addition, stimulation increased NQO1 protein compared to contralateral control regardless of stimulation intensity (p = 0.019), and HO1 protein was significantly higher in high intensity compared to the Naive control group (p = 0.002). Taken together, these data suggest a systemic redox signaling exerkine is activating Nrf2-ARE binding and is intensity gated, where Nrf2-ARE activation in contralateral control limbs were only seen in the HI group. Other research in exercise induced Nrf2 signaling support the general finding that Nrf2 is activated in peripheral tissues in response to exercise, however the specific exerkine responsible for the systemic signaling effects is not known. Future work should aim to delineate these redox sensitive systemic signaling mechanisms.
Comparison on two ways of phonon spectral property reconstruction from thermal spectroscopy experiment

Abstract We examine the reliability of the cumulative thermal conductivity as a function of the free path, F ( Λ ) , in the context of reconstruction of phonon transport properties from thermal spectroscopy experiments. We specifically show that a given F ( Λ ) does not correspond to a unique relaxation-times function, in the sense that more than one distribution of relaxation-times can result in the same F ( Λ ) . Since different relaxation-time distributions will, in general, lead to different thermal responses, F ( Λ ) does not uniquely predict the material thermal response in all transport regimes. This implies that in the context of thermal transport at the nanoscale within the Boltzmann relaxation-time approximation framework, the “fundamental” property that should be reconstructed from the thermal spectroscopy experiments is the frequency-dependent relaxation-times function (provided group velocities are known), since it explicitly appears in the governing equation as the input material property. Extensive global optimization studies show that a previously proposed formulation for reconstruction based on the frequency-dependent relaxation-times function [Physical Review B 94, 155439 (2016)] provides numerically unique solutions.
Consumers as Financiers: Consumer Surplus, Crowdfunding, and Initial Coin Offerings

We study the efficiency implications of funding directly provided by consumers. Intermediaries fail to finance all efficient projects, and crowdfunding can improve efficiency. Whereas intermediaries value projects based on cash flows, consumers also receive a consumption benefit. Unique to crowdfunding is the ability of consumers to commit to pay for the benefit, and the degree to which they can do so determines its efficiency. We discuss the implications of introducing a resale market for consumers' claims, as in the case of initial coin offerings, and the speculation that necessarily accompanies such markets. We provide testable and policy-related implications.
HackerScope: the dynamics of a massive hacker online ecosystem

One would have thought that hackers would be striving to hide from public view, but we find that this is not the case: they have a public online footprint. Apart from online security forums, this footprint appears also in software development platforms, where authors create publicly accessible malware repositories to share and collaborate. With the exception of a few recent efforts, the existence and the dynamics of this community has received surprisingly limited attention. The goal of our work is to analyze this ecosystem of hackers in order to: (a) understand their collaborative patterns and (b) identify and profile its most influential authors. We develop HackerScope, a systematic approach for analyzing the dynamics of this hacker ecosystem. Leveraging our targeted data collection, we conduct an extensive study of 7389 authors of malware repositories on GitHub, which we combine with their activity on four security forums. From a modelling point of view, we study the ecosystem using three network representations: (a) the author-author network, (b) the author-repository network and (c) cross-platform egonets. Our analysis leads to the following key observations: (a) the ecosystem is growing at an accelerating rate as the number of new malware authors per year triples every 2 years, (b) it is highly collaborative, more so than the rest of GitHub authors, and (c) it includes influential and professional hackers. We find 101 authors maintain an online “brand” across GitHub and our online forums. Our study is a significant step towards using public online information for understanding the malicious hacker community.
Per- and Polyfluoroalkyl Substances in Drinking Water and Birthweight in the US: A County-level Study

Background: Per- and polyfluoroalkyl substances (PFAS) are ubiquitous in the serum of general US population, and were detected in public water systems serving approximately 16.5 million US resident...
Linear subspaces of hypersurfaces

Let X be an arbitrary smooth hypersurface in CPn of degree d. We prove the de Jong–Debarre conjecture for n≥2d−4: the space of lines in X has dimension 2n−d−3. We also prove an analogous result for k-planes: if n≥2d+k−1k+k, then the space of k-planes on X will be irreducible of the expected dimension. As applications, we prove that an arbitrary smooth hypersurface satisfying n≥2d! is unirational, and we prove that the space of degree-e curves on X will be irreducible of the expected dimension provided that d≤e+n e+1.
Learning on a Grassmann Manifold: CSI Quantization for Massive MIMO Systems

This paper focuses on the design of beamforming codebooks that maximize the average normalized beamforming gain for any underlying channel distribution. While the existing techniques use statistical channel models, we utilize a model-free data-driven approach with foundations in machine learning to generate beamforming codebooks that adapt to the surrounding propagation conditions. The key technical contribution lies in reducing the codebook design problem to an unsupervised clustering problem on a Grassmann manifold where the cluster centroids form the finite-sized beamforming codebook for the channel state information (CSI), which can be efficiently solved using K-means clustering. This approach is extended to develop a remarkably efficient procedure for designing product codebooks for full-dimension (FD) multiple-input multiple-output (MIMO) systems with uniform planar array (UPA) antennas. Simulation results demonstrate the capability of the proposed design criterion in learning the codebooks, reducing the codebook size and producing noticeably higher beamforming gains compared to the existing state-of-the-art CSI quantization techniques.
Tribological characteristics of gold films deposited on metals by ion plating and vapor deposition

Abstract Recent work on the graded interface between an ion-plated film and a substrate is discussed as well as the friction and wear properties of ionplated gold. X-ray photoelectron spectroscopy, depth profiling and microhardness measurements were used to investigate the interface. The friction and wear properties of ion-plated and vapor-deposited gold films were studied both in an ultrahigh vacuum system to maximize adhesion and in oil to minimize adhesion. The results of the investigation indicate that the solubility of gold in the substrate material controls the depth of the graded interface. Thermal and chemical diffusion mechanisms are thought to be involved in the formation of the Au-Ni interface. In the Fe-Au graded inter- face, gold was slightly dispersed in the iron and formed only a physically bonded interface. The hardness of the gold film was influenced by the thickness and was also related to the composition gradient between the gold and the substrate. A graded Ni-Au interface exhibited the highest hardness because of an alloy hardening effect. The effects of film thickness on adhesion and friction were established. A minimum coefficient of friction was found in the thin film region. No graded interfaces were detected in this investigation between vapor-deposited gold films and substrates.
Laser Velocimeter Measurements of the Helicopter Rotor‐Induced Flow Field

The use of a two-color laser velocimeter to measure the flow velocities in the wake of a helicopter rotor is discussed, including methods for obtaining two components of both instantaneous and time-averaged velocities. Results are presented from an experiment using a 2.13 m diameter model helicopter rotor operating at a tip speed ratio of 0.18 in a wind tunnel. The location of the tip vortex from the preceding blade was determined on the advancing side, and the diameter of the vortex core was found to be 15 percent of the blade chord (1.5 percent of the radius). The effects of the airfoil's bound vorticity were observed in the velocity distributions vry near the blade. These effects suggest that the laser velocimeter may be used to determine the aerodynamic loading (circulation) at a spanwise station on the blade. Also, the structure and boundary of the time-averaged wake were investigated.
Genetic and phylogenetic analysis of dissimilatory iodate-reducing bacteria identifies potential niches across the world's oceans.

Iodine is oxidized and reduced as part of a biogeochemical cycle that is especially pronounced in the oceans, where the element naturally concentrates. The use of oxidized iodine in the form of iodate (IO3 −) as an electron acceptor by microorganisms is poorly understood. Here, we outline genetic, physiological, and ecological models for dissimilatory IO3 − reduction to iodide (I−) by a novel estuarine bacterium, Denitromonas sp. IR-12. Our results show that dissimilatory iodate reduction (DIR) by strain IR-12 is molybdenum-dependent and requires an IO3 − reductase (idrA) and likely other genes in a mobile cluster with a conserved association across known and predicted DIR microorganisms (DIRM). Based on genetic and physiological data, we propose a model where three molecules of IO3 − are likely reduced to three molecules of hypoiodous acid (HIO), which rapidly disproportionate into one molecule of IO3 − and two molecules of iodide (I−), in a respiratory pathway that provides an energy yield equivalent to that of nitrate or perchlorate respiration. Consistent with the ecological niche expected of such a metabolism, idrA is enriched in the metagenome sequence databases of marine sites with a specific biogeochemical signature (high concentrations of nitrate and phosphate) and diminished oxygen. Taken together, these data suggest that DIRM help explain the disequilibrium of the IO3 −:I− concentration ratio above oxygen-minimum zones and support a widespread iodine redox cycle mediated by microbiology.
Status of the L2 and Lunar Charged Particle Environment Models

The L2 Charged Particle Environment (L2-CPE) model is an engineering tool which provides free field charged particle environments for distant magnetotail, magnetosheath, and solar wind environments. L2-CPE is intended for use in assessing contributions from low energy radiation environments (4.1 keV to few MeV) to radiation dose in thin materials used in construction of spacecraft to be placed in orbit about the Sun-Earth L2 point. This paper describes the status of the current version of the L2-CPE model including structure of the model used to organize plasma environments into solar wind, magnetosheath, and magnetotail environments, the algorithms used to estimate radiation fluence in sparsely sampled environments, the updated graphical user interface, and output options for flux and fluence environments. In addition, we describe the status and plans for updating the model to include environments relevant to lunar programs.
Shortformer: Better Language Modeling using Shorter Inputs

Increasing the input length has been a driver of progress in language modeling with transformers. We identify conditions where shorter inputs are not harmful, and achieve perplexity and efficiency improvements through two new methods that decrease input length. First, we show that initially training a model on short subsequences before moving on to longer ones both reduces overall training time and, surprisingly, substantially improves perplexity. Second, we show how to improve the efficiency of recurrence methods in transformers, which let models condition on previously processed tokens when generating sequences that exceed the maximal length the transformer can handle at once. Existing methods require computationally expensive relative position embeddings; we introduce a simple alternative of adding absolute position embeddings to queries and keys instead of to word embeddings, which efficiently produces superior results. We show that these recurrent models also benefit from short input lengths. Combining these techniques speeds up training by a factor of 1.65, reduces memory usage, and substantially improves perplexity on WikiText-103, without adding any parameters.
Natural Laminar Flow Design for Wings with Moderate Sweep

A new method for the aerodynamic design of wings with natural laminar flow is under development at the NASA Langley Research Center. The approach involves the addition of new flow constraints to an existing knowledge-based design module for use with advanced flow solvers. The uniqueness of the new approach lies in the tailoring of target pressure distributions to achieve laminar flow on transonic wings with leading-edge sweeps and Reynolds numbers typical of current transports. The method is demonstrated on the Common Research Model configuration at critical N-factor levels representative of both flight and high-Reynolds number wind tunnel turbulence levels. The design results for the flight conditions matched the target extent of laminar flow very well. The design at wind tunnel conditions raised some design issues that prompted further improvements in the method, but overall has given promising results.
On a capacitary strong type inequality and related capacitary estimates

We establish a Maz'ya type capacitary inequality which resolves a special case of a conjecture by David R. Adams. As a consequence, we obtain several equivalent norms for Choquet integrals associated to Bessel or Riesz capacities. This enables us to obtain bounds for the Hardy-Littlewood maximal function in a sublinear setting.
When All That Glitters Is Gold: Dominated Plan Choice on Covered California for the 2018 Plan Year.

Policy Points Purchasing health insurance is a complex task with multiple potential points of failure. In 2018, following the silver-loading price shock, 20.2% of households earning above 200% of the federal poverty level with coverage through the two Covered California insurers who sold dominated silver plans purchased the inferior, dominated silver plan. Individuals who were automatically reenrolled were more likely to purchase an inferior, dominated plan. Automatic reenrollment rules and marketplace choice architecture should be modified to avoid placing people into dominated health insurance policies and help consumers more easily select superior coverage for themselves. CONTEXT The Affordable Care Act (ACA) individual health insurance marketplaces rely on purchasers to make informed choices to impose price and quality discipline on a complex array of insurance products. A sudden and minimally expected policy shock in the fall of 2017-the termination of direct federal payment for cost-sharing reduction (CSR) subsidies-led to a substantial change in the relative prices of silver and gold plans on the Covered California insurance marketplace. From 2014 to 2017, all gold plans in California were more expensive than comparable silver plans that were offered by the same insurer using the same network in the same county. For the 2018 plan year, however, some gold plans that had lower cost sharing also had lower premiums than did comparable silver plans, resulting in silver "dominated" plans being sold through Covered California. METHODS We used the Covered California enrollment and product files from 2014 to 2018 in a retrospective data analysis of plan choice. We examined individuals earning above 200% of the federal poverty level who purchased plans from insurers who sold dominated silver plans in 2018. FINDINGS We found that 3.9% of all Covered California enrollees in 2018 chose a strictly and transparently dominated plan. Among households with incomes above 200% of the federal poverty level that were enrolled in plans from the two insurers that offered dominated plans, 20.2% chose a dominated plan. Households that actively enrolled in 2018 and were enrolled in a silver plan in the previous year enrolled in a dominated plan at higher rates than did new enrollees and those who were enrolled in nonsilver plans in the previous year. More than 30% of households that had their coverage automatically renewed in 2018 enrolled in a dominated plan. On average, households enrolled in dominated plans in 2018 spent an additional $38.87 per month in premiums. CONCLUSIONS Households routinely chose dominated plans and were exposed to both higher monthly premiums and higher potential cost sharing. Health insurance marketplaces should improve decision supports and choice curation to eliminate the possibility of individuals choosing dominated plans.
Enhanced U-Net: A Feature Enhancement Network for Polyp Segmentation

Colonoscopy is a procedure to detect colorectal polyps which are the primary cause for developing colorectal cancer. However, polyp segmentation is a challenging task due to the diverse shape, size, color, and texture of polyps, shuttle difference between polyp and its background, as well as low contrast of the colonoscopic images. To address these challenges, we propose a feature enhancement network for accurate polyp segmentation in colonoscopy images. Specifically, the proposed network enhances the semantic information using the novel Semantic Feature Enhance Module (SFEM). Furthermore, instead of directly adding encoder features to the respective decoder layer, we introduce an Adaptive Global Context Module (AGCM), which focuses only on the encoder’s significant and hard fine-grained features. The integration of these two modules improves the quality of features layer by layer, which in turn enhances the final feature representation. The proposed approach is evaluated on five colonoscopy datasets and demonstrates superior performance compared to other state-of-the-art models.
Learning-based Robust Motion Planning With Guaranteed Stability: A Contraction Theory Approach

This letter presents Learning-based Autonomous Guidance with RObustness and Stability guarantees (LAG-ROS), which provides machine learning-based nonlinear motion planners with formal robustness and stability guarantees, by designing a differential Lyapunov function using contraction theory. LAG-ROS utilizes a neural network to model a robust tracking controller independently of a target trajectory, for which we show that the Euclidean distance between the target and controlled trajectories is exponentially bounded linearly in the learning error, even under the existence of bounded external disturbances. We also present a convex optimization approach that minimizes the steady-state bound of the tracking error to construct the robust control law for neural network training. In numerical simulations, it is demonstrated that the proposed method indeed possesses superior properties of robustness and nonlinear stability resulting from contraction theory, whilst retaining the computational efficiency of existing learning-based motion planners.
Optimal Advertising for Information Products

When selling information products, sometimes the seller can provide some free partial information to change people's valuations so that the overall revenue can possibly be increased. In this work, we study the general problem of advertising information products by revealing partial information. We consider buyers who are decision-makers. The outcomes of the decision problems depend on the state of the world that is unknown to the buyers. The buyers can make their own observations and thus can hold different personal beliefs about the state of the world. There is an information seller who has access to the state of the world. The seller can promote the information by revealing some partial information. We assume that the seller chooses a long-term advertising strategy and then commits to it. The buyers decide whether to purchase the full information product after seeing the partial information. The seller's goal is to maximize the expected revenue. We study the problem in two settings. (1) The seller targets buyers of a certain type. In this case, we prove that finding the optimal advertising strategy is equivalent to finding the concave closure of a simple function. The function is a product of two quantities. The first one is what we call the likelihood ratio, which depends on the buyer's personal belief about the state of the world. The second one is the cost of uncertainty, which represents the value of the information to the buyer. Based on this observation, we prove some properties of the optimal mechanism, which allow us to solve for the optimal mechanism by a finite-size convex program. The convex program will have a polynomial size if the state of the world has a constant number of possible realizations or the buyers face a decision problem with a constant number of options. For the general problem, we prove that it is NP-hard to find the optimal mechanism. (2) For the general problem when the seller faces buyers of different types and only knows the distribution of their types, we provide an approximation algorithm when it is not too hard to predict the possible type of buyers who will make the purchase. For the general problem, we prove that it is NP-hard to find a constant-factor approximation.
Memory Lens – How Much Memory Does an Agent Use?

We propose a new method to study the internal memory used by reinforcement learning policies. We estimate the amount of relevant past information by estimating mutual information between behavior histories and the current action of an agent. We perform this estimation in the passive setting, that is, we do not intervene but merely observe the natural behavior of the agent. Moreover, we provide a theoretical justification for our approach by showing that it yields an implementation-independent lower bound on the minimal memory capacity of any agent that implement the observed policy. We demonstrate our approach by estimating the use of memory of DQN policies on concatenated Atari frames, demonstrating sharply different use of memory across 49 games. The study of memory as information that flows from the past to the current action opens avenues to understand and improve successful reinforcement learning algorithms.
High Order Control Lyapunov-Barrier Functions for Temporal Logic Specifications

Recent work has shown that stabilizing an affine control system to a desired state while optimizing a quadratic cost subject to state and control constraints can be reduced to a sequence of Quadratic Programs (QPs) by using Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs). In our own recent work, we defined High Order CBFs (HOCBFs) for systems and constraints with arbitrary relative degrees. In this paper, in order to accommodate initial states that do not satisfy the state constraints and constraints with arbitrary relative degree, we generalize HOCBFs to High Order Control Lyapunov-Barrier Functions (HOCLBFs). We also show that the proposed HOCLBFs can be used to guarantee the Boolean satisfaction of Signal Temporal Logic (STL) formulae over the state of the system. We illustrate our approach on a safety-critical optimal control problem (OCP) for a unicycle.
Dynamics of Florida milk production and total phosphate in Lake Okeechobee

A central tenant of the Comprehensive Everglades Restoration Plan (CERP) is nutrient reduction to levels supportive of ecosystem health. A particular focus is phosphorus. We examine links between agricultural production and phosphorus concentration in the Everglades headwaters: Kissimmee River basin and Lake Okeechobee, considered an important source of water for restoration efforts. Over a span of 47 years we find strong correspondence between milk production in Florida and total phosphate in the lake, and, over the last decade, evidence that phosphorus concentrations in the lake water column may have initiated a long-anticipated decline.
TEAM: Trilateration for Exploration and Mapping with Robotic Networks

Motivated by lunar exploration, we consider deploying a network of mobile robots to explore an unknown environment while acting as a cooperative positioning system. Robots measure and communicate position-related data in order to perform localization in the absence of infrastructure-based solutions (e.g. stationary beacons or GPS). We present Trilateration for Exploration and Mapping (TEAM), a novel algorithm for low-complexity localization and mapping with robotic networks. TEAM is designed to leverage the capability of commercially-available ultra-wideband (UWB) radios on board the robots to provide range estimates with centimeter accuracy and perform anchorless localization in a shared, stationary frame. It is well-suited for feature-deprived environments, where feature-based localization approaches suffer. We provide experimental results in varied Gazebo simulation environments as well as on a testbed of Turtlebot3 Burgers with Pozyx UWB radios. We compare TEAM to the popular Rao-Blackwellized Particle Filter for Simultaneous Localization and Mapping (SLAM). We demonstrate that TEAM requires an order of magnitude less computational complexity and reduces the necessary sample rate of LiDAR measurements by an order of magnitude. These advantages do not require sacrificing performance, as TEAM reduces the maximum localization error by 50% and achieves up to a 28% increase in map accuracy in feature-deprived environments and comparable map accuracy in other settings.
Digger Finger: GelSight Tactile Sensor for Object Identification Inside Granular Media

Imagine you are at the beach with a metal detector which goes off, and you stick your hands in the sand to find the metal object. Even though the granular media (sand) is constantly affecting your sense of touch on your fingers and palm, its acuity combined with your cognition enables you to easily find buried objects