cssi/SciSciGPT-SciSciNet huggingface dataset:
Harmonic-balance circuit analysis for electro-neural interfaces.

Objective Avoidance of the adverse electrochemical reactions at the electrode-electrolyte interface defines the voltage safety window and limits the charge injection capacity (CIC) of an electrode material. For an electrode that is not ideally capacitive, the CIC depends on the waveform of the stimulus. We study the modeling of the charge injection dynamics to optimize the waveforms for efficient neural stimulation within the electrochemical safety limits. Approach The charge injection dynamics at the electrode-electrolyte interface is typically characterized by the electrochemical impedance spectrum, and is often approximated by discrete-element circuit models. We compare the modeling of the complete circuit, including a non-linear driver such as a photodiode, based on the harmonic-balance (HB) analysis with the analysis based on various - (discrete-element) approximations. To validate the modeling results, we performed experiments with iridium-oxide electrodes driven by a current source with diodes in parallel, which mimics a photovoltaic circuit. Main results Application of HB analysis based on a full impedance spectrum eliminates the complication of finding the discrete-element circuit model in traditional approaches. HB-based results agree with the experimental data better than the discrete-element circuit. HB technique can be applied not only to demonstrate the circuit response to periodic stimulation, but also to describe the initial transient behavior when a burst waveform is applied. Significance HB-based circuit analysis accurately describes the dynamics of electrode-electrolyte interfaces and driving circuits for all pulsing schemes. This allows optimizing the stimulus waveform to maximize the CIC, based on the impedance spectrum alone.
Timing and Specificity of Cotranslational Nascent Protein Modification in Bacteria

The nascent polypeptide exit site of the ribosome is a crowded environment where multiple ribosome-associated protein biogenesis factors (RPBs) compete for the nascent polypeptide to influence their localization, folding, or quality control. Here we address how N-terminal methionine excision (NME), a ubiquitous process crucial for the maturation of over 50% of the bacterial proteome, occurs in a timely and selective manner in this crowded environment. In bacteria, NME is mediated by 2 essential enzymes, peptide deformylase (PDF) and methionine aminopeptidase (MAP). We show that the reaction of MAP on ribosome-bound nascent chains approaches diffusion-limited rates, allowing immediate methionine excision of optimal substrates after deformylation. Specificity is achieved by kinetic competition of NME with translation elongation and by regulation from other RPBs, which selectively narrow the processing time window for suboptimal substrates. A mathematical model derived from the data accurately predicts cotranslational NME efficiency in the cytosol. Our results demonstrate how a fundamental enzymatic activity is reshaped by its associated macromolecular environment to optimize both efficiency and selectivity, and provides a platform to study other cotranslational protein biogenesis pathways.
Guns to butter: sociotropic concerns and foreign policy preferences in Russia

On the concept of “rally around the flag,” scholars often argue that by invoking the danger of external threats in times of economic hardship, leaders can rally the public around the government in ...
A simultaneous version of Host’s equidistribution Theorem

Let $\mu$ be a probability measure on $\mathbb{R}/\mathbb{Z}$ that is ergodic under the $\times p$ map, with positive entropy. In 1995, Host showed that if $\gcd(m,p)=1$ then $\mu$ almost every point is normal in base $m$. In 2001, Lindenstrauss showed that the conclusion holds under the weaker assumption that $p$ does not divide any power of $m$. In 2015, Hochman and Shmerkin showed that this holds in the "correct" generality, i.e. if $m$ and $p$ are independent. We prove a simultaneous version of this result: for $\mu$ typical $x$, if $m>p$ are independent, we show that the orbit of $(x,x)$ under $(\times m, \times p)$ equidistributes for the product of the Lebesgue measure with $\mu$. We also show that if $m>n>1$ and $n$ is independent of $p$ as well, then the orbit of $(x,x)$ under $(\times m, \times n)$ equidistributes for the Lebesgue measure.
The Mertens Unrolled Network (MU-Net): A High Dynamic Range Fusion Neural Network for Through the Windshield Driver Recognition

Face recognition of vehicle occupants through windshields in unconstrained environments poses a number of unique challenges ranging from glare, poor illumination, driver pose and motion blur. In this paper, we further develop the hardware and software components of a custom vehicle imaging system to better overcome these challenges. After the build out of a physical prototype system that performs High Dynamic Range (HDR) imaging, we collect a small dataset of through-windshield image captures of known drivers. We then reformulate the classical Mertens-Kautz-Van Reeth HDR fusion algorithm as a pre-initialized neural network, which we name the Mertens Unrolled Network (MU-Net), for the purpose of fine-tuning the HDR output of through-windshield images. Reconstructed faces from this novel HDR method are then evaluated and compared against other traditional and experimental HDR methods in a pre-trained state-of-the-art (SOTA) facial recognition pipeline, verifying the efficacy of our approach.
Tied Survival Times

Tied survival times occur in survival studies frequently. We discuss adjustment for ties for some common statistical procedures. Keywords: Cox regression; Kaplan–Meier estimator; logistic regression; logrank test; tied survival times
Conception, Models for†

Time to conception offers a stochastic surrogate for fertility, in that subfertile couples not using contraception tend to take longer to conceive than do fertile couples. Couples can be studied retrospectively, by asking about how long it took them to achieve an index pregnancy, or prospectively, by recruiting couples during or prior to an attempt to conceive. The resulting data play out in discrete time, as measured by the number of menstrual cycles required. Because of heterogeneity among couples in their fecundability (per cycle probability of conception), the data are overdispersed compared to the geometric. This overdispersion can be modeled parametrically (e.g. with a beta-geometric) or nonparametrically (based on the moments of the dispersed but unknown underlying fecundability distribution). If more detailed data are available, on days with intercourse and time of ovulation, then the models for conception can additionally incorporate the strong effects of timing. Keywords: heterogeneity; fecundability; time to pregnancy; waiting time to conception; fertility; EM algorithm
Interconnection Networks for Parallel Computers

The interconnection network is responsible for fast and reliable communication among the processing nodes in any parallel computer. This article gives a brief introduction to some of the concepts involved in the design of interconnection networks for parallel machines. Keywords: network topologies; direct networks; indirect networks; hops; switching; routing
Heat or eat? Cold-weather shocks and nutrition in poor American families.

Objectives. The authors sought to determine the effects of cold-weather periods on budgets and nutritional outcomes among poor American families. Methods. The Consumer Expenditure Survey was used to track expenditures on food and home fuels, and the Third National Health and Nutrition Examination Survey was used to track calorie consumption, dietary quality, vitamin deficiencies, and anemia. Results. Both poor and richer families increased fuel expenditures in response to unusually cold weather. Poor families reduced food expenditures by roughly the same amount as their increase in fuel expenditures, whereas richer families increased food expenditures. Conclusions. Poor parents and their children spend less on and eat less food during cold-weather budgetary shocks. Existing social programs fail to buffer against these shocks.
Giotto: A Time-Triggered Language for Embedded Programming

Giotto provides an abstract programmer's model for the implementation of embedded control systems with hard real-time constraints. A typical control application consists of periodic software tasks together with a mode switching logic for enabling and disabling tasks. Giotto specifies time-triggered sensor readings, task invocations, and mode switches independent of any implementation platform. Giotto can be annotated with platform constraints such as task-to-host mappings, and task and communication schedules. The annotations are directives for the Giotto compiler, but they do not alter the functionality and timing of a Giotto program. By separating the platform-independent from the platform-dependent concerns, Giotto enables a great deal of flexibility in choosing control platforms as well as a great deal of automation in the validation and synthesis of control software. The time-triggered nature of Giotto achieves timing predictability, which makes Giotto particularly suitable for safety-critical applications.
Wall-Crossings for Hassett Descendant Potentials

This paper solves the combinatorics relating the intersection theory of $\psi$-classes of Hassett spaces to that of $\overline{\mathcal{M}}_{g,n}$. A generating function for intersection numbers of $\psi$ classes on all Hassett spaces is obtained from the Gromov-Witten potential of a point via a non-invertible transformation of variables. When restricting to diagonal weights, the changes of variables are invertible and explicitly described as polynomial functions. Finally, the comparison of potentials is extended to the level of cycles: the pinwheel cycle potential, a generating function for tautological classes of rational tail type on $\overline{\mathcal{M}}_{g,n}$ is the right instrument to describe the pull-back to $\overline{\mathcal{M}}_{g,n}$ of all monomials of $\psi$ classes on Hassett spaces.
The Demand for Money in Korea: Evidence from the Cointegration Test

This paper shows that there exists a long-run equilibrium relationship between M2 and its determinants, real income and the long-term interest rate, in Korea by using Johansen and Juselius maximum likelihood cointegration method. However, M1 does not have any meaningful cointegration relationships with its determinants. The long-term interest rate is a better proxy than the short-term rate to measure the opportunity cost of holding money. Based on the results, a broad definition of money is a better measure than a narrow definition of money in considering the long-run economic impacts of changes in monetary policy in Korea.
Heat-Diffusion: Pareto Optimal Dynamic Routing for Time-Varying Wireless Networks

A dynamic routing policy, referred to as Heat-Diffusion (HD), is developed for multihop uniclass wireless networks subject to random traffic, time-varying topology and inter-channel interference. The policy uses only current condition of queue occupancies and channel states, with requiring no knowledge of traffic and topology. Besides throughput optimality, HD minimizes an average quadratic routing cost defined by endowing each channel with a time-varying cost factor. Further, HD minimizes average network delay in the class of routing policies that base decisions only on current condition of traffic congestion and channel states. Further, in this class of routing policies, HD provides a Pareto optimal tradeoff between average routing cost and average network delay, meaning that no policy can improve either one without detriment to the other. Finally, HD fluid limit follows graph combinatorial heat equation, which can open a new way to study wireless networks using heat calculus, a very active area of pure mathematics.
Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles

A fundamental challenge in contextual bandits is to develop flexible, general-purpose algorithms with computational requirements no worse than classical supervised learning tasks such as classification and regression. Algorithms based on regression have shown promising empirical success, but theoretical guarantees have remained elusive except in special cases. We provide the first universal and optimal reduction from contextual bandits to online regression. We show how to transform any oracle for online regression with a given value function class into an algorithm for contextual bandits with the induced policy class, with no overhead in runtime or memory requirements. We characterize the minimax rates for contextual bandits with general, potentially nonparametric function classes, and show that our algorithm is minimax optimal whenever the oracle obtains the optimal rate for regression. Compared to previous results, our algorithm requires no distributional assumptions beyond realizability, and works even when contexts are chosen adversarially.
Logical Natural Language Generation from Open-Domain Tables

Neural natural language generation (NLG) models have recently shown remarkable progress in fluency and coherence. However, existing studies on neural NLG are primarily focused on surface-level realizations with limited emphasis on logical inference, an important aspect of human thinking and language. In this paper, we suggest a new NLG task where a model is tasked with generating natural language statements that can be logically entailed by the facts in an open-domain semi-structured table. To facilitate the study of the proposed logical NLG problem, we use the existing TabFact dataset~(CITATION) featured with a wide range of logical/symbolic inferences as our testbed, and propose new automatic metrics to evaluate the fidelity of generation models w.r.t. logical inference. The new task poses challenges to the existing monotonic generation frameworks due to the mismatch between sequence order and logical order. In our experiments, we comprehensively survey different generation architectures (LSTM, Transformer, Pre-Trained LM) trained with different algorithms (RL, Adversarial Training, Coarse-to-Fine) on the dataset and made following observations: 1) Pre-Trained LM can significantly boost both the fluency and logical fidelity metrics, 2) RL and Adversarial Training are trading fluency for fidelity, 3) Coarse-to-Fine generation can help partially alleviate the fidelity issue while maintaining high language fluency. The code and data are available at https://github.com/wenhuchen/LogicNLG.
USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation

The lack of meaningful automatic evaluation metrics for dialog has impeded open-domain dialog research. Standard language generation metrics have been shown to be ineffective for evaluating dialog models. To this end, this paper presents USR, an UnSupervised and Reference-free evaluation metric for dialog. USR is a reference-free metric that trains unsupervised models to measure several desirable qualities of dialog. USR is shown to strongly correlate with human judgment on both Topical-Chat (turn-level: 0.42, system-level: 1.0) and PersonaChat (turn-level: 0.48 and system-level: 1.0). USR additionally produces interpretable measures for several desirable properties of dialog.
An improved cutting plane method for convex optimization, convex-concave games, and its applications

Given a separation oracle for a convex set K ⊂ ℝ n that is contained in a box of radius R, the goal is to either compute a point in K or prove that K does not contain a ball of radius є. We propose a new cutting plane algorithm that uses an optimal O(n log(κ)) evaluations of the oracle and an additional O(n 2) time per evaluation, where κ = nR/є. This improves upon Vaidya’s O( SO · n log(κ) + n ω+1 log(κ)) time algorithm [Vaidya, FOCS 1989a] in terms of polynomial dependence on n, where ω O( SO · n log(κ) + n 3 log O(1) (κ)) time algorithm [Lee, Sidford and Wong, FOCS 2015] in terms of dependence on κ. For many important applications in economics, κ = Ω(exp(n)) and this leads to a significant difference between log(κ) and (log(κ)). We also provide evidence that the n 2 time per evaluation cannot be improved and thus our running time is optimal. A bottleneck of previous cutting plane methods is to compute leverage scores, a measure of the relative importance of past constraints. Our result is achieved by a novel multi-layered data structure for leverage score maintenance, which is a sophisticated combination of diverse techniques such as random projection, batched low-rank update, inverse maintenance, polynomial interpolation, and fast rectangular matrix multiplication. Interestingly, our method requires a combination of different fast rectangular matrix multiplication algorithms. Our algorithm not only works for the classical convex optimization setting, but also generalizes to convex-concave games. We apply our algorithm to improve the runtimes of many interesting problems, e.g., Linear Arrow-Debreu Markets, Fisher Markets, and Walrasian equilibrium.
Parallel approximate undirected shortest paths via low hop emulators

We present a (1+e)-approximate parallel algorithm for computing shortest paths in undirected graphs, achieving poly(logn) depth and m poly(logn) work for n-nodes m-edges graphs. Although sequential algorithms with (nearly) optimal running time have been known for several decades, near-optimal parallel algorithms have turned out to be a much tougher challenge. For (1+e)-approximation, all prior algorithms with poly(logn) depth perform at least Ω(mn c ) work for some constant c>0. Improving this long-standing upper bound obtained by Cohen (STOC’94) has been open for 25 years. We develop several new tools of independent interest. One of them is a new notion beyond hopsets — low hop emulator — a poly(logn)-approximate emulator graph in which every shortest path has at most O(loglogn) hops (edges). Direct applications of the low hop emulators are parallel algorithms for poly(logn)-approximate single source shortest path (SSSP), Bourgain’s embedding, metric tree embedding, and low diameter decomposition, all with poly(logn) depth and m poly(logn) work. To boost the approximation ratio to (1+e), we introduce compressible preconditioners and apply it inside Sherman’s framework (SODA’17) to solve the more general problem of uncapacitated minimum cost flow (a.k.a., transshipment problem). Our algorithm computes a (1+e)-approximate uncapacitated minimum cost flow in poly(logn) depth using m poly(logn) work. As a consequence, it also improves the state-of-the-art sequential running time from m· 2 O(√logn) to m poly(logn).
Fixed-Time Stable Gradient Flows: Applications to Continuous-Time Optimization

Continuous-time optimization is currently an active field of research in optimization theory; prior work in this area has yielded useful insights and elegant methods for proving stability and convergence properties of the continuous-time optimization algorithms. This article proposes novel gradient-flow schemes that yield convergence to the optimal point of a convex optimization problem within a fixed time from any given initial condition for unconstrained optimization, constrained optimization, and min–max problems. It is shown that the solution of the modified gradient-flow dynamics exists and is unique under certain regularity conditions on the objective function, while fixed-time convergence to the optimal point is shown via Lyapunov-based analysis. The application of the modified gradient flow to unconstrained optimization problems is studied under the assumption of gradient dominance, a relaxation of strong convexity. Then, a modified Newton's method is presented that exhibits fixed-time convergence under some mild conditions on the objective function. Building upon this method, a novel technique for solving convex optimization problems with linear equality constraints that yields convergence to the optimal point in fixed time is developed. Finally, the general min–max problem is considered, and a modified saddle-point dynamics to obtain the optimal solution in fixed time is developed.
Covid-19 and the credit cycle

The Covid-19 health crisis has dramatically affected just about every aspect of the economy, including the transition from a record long benign credit cycle to a stressed one, with still uncertain dimensions This paper seeks to assess the credit climate from just before the unexpected global health crisis catalyst to its immediate and extended impact We analyze the performance of several key indicators of the nature of credit cycles: default and recovery rates on high-yield bonds, and the number of large firm bankruptcies that we expect over the next twelve months and beyond;yield spreads and distress ratios;and liquidity Our focus is primarily on the nonfi-nancial corporate debt market in the United States, which reached a record percent-age of gross domestic product at the end of 2019 as firms increased their debt to take advantage of record low interest rates, and investor appetite grew for higher promised yields on risky fixed-income assets We also examine the leveraged loan and collater-alized loan obligation markets, as well as the increasingly large and important BBB tranche of the corporate bond market Specifically, we discuss the latter’s vulnerabil-ity to downgrades over the expected downturn in the real economy and this vulnera-bility’s potential impact on expected default rates by “crowding out” low-quality debt of other firms (some of which we believe are “zombies”) Using Z-scores for a sample of BBB companies between 2007 and 2019, we analyze this largest component of the corporate bond market to provide some evidence on the controversial debate as to whether there has been ratings inflation or, perhaps, persistent overvaluation of the nonfinancial corporate debt market since the last financial crisis © 2020 Infopro Digital Risk (IP) Limited
Beyond the Motor Cortex: Theta Burst Stimulation of the Anterior Midcingulate Cortex.

Abstract Background While the facilitatory and inhibitory effects of intermittent theta burst stimulation (iTBS) and continuous TBS (cTBS) protocols have been well documented on motor physiology, the action of TBS protocols on prefrontal functioning remain unclear. Here we asked whether iTBS or cTBS can differentially modulate reward-related signaling in the anterior midcingulate cortex (aMCC). Methods Across 2 experiments, we used a robot-assisted transcranial magnetic stimulation system, combined with electroencephalogram recordings, to investigate the aftereffects of prefrontal iTBS and cTBS on the reward positivity, an electrophysiological signal believed to index sensitivity of the aMCC to rewards. Twenty adults (age, 18–28 years) participated in experiment 1 in which we used a scalp landmark for TBS targeting, and 14 adults (age, 18–28 years) participated in experiment 2, in which we aimed to increase TBS effectiveness by utilizing cortical thickness maps to select individualized dorsal lateral prefrontal cortex targets. Results We demonstrated that prefrontal iTBS suppressed reward-related signaling in the aMCC (reduction in reward positivity) and caused a decrease in postfeedback switch choices. cTBS displayed no effect. We replicated and strengthened this effect on the reward positivity by targeting dorsal lateral prefrontal cortex regions displaying maximal cortical thickness. Conclusions While these results are inconsistent with reported TBS effects on motor cortex, the present findings offer a novel transcranial magnetic stimulation targeting approach and normative insights into the magnitude and time course of TBS-induced changes in aMCC excitability. By modulating how the aMCC links value to goal-directed behavior, this research opens an exciting new era of investigative possibilities in the understanding of aMCC function and treatment of aMCC dysfunction.
A scalable framework for solving fractional diffusion equations

The study of fractional order differential operators (involving non-integer derivative terms) is receiving renewed attention in many scientific fields from photonics to speech modeling. While numerous scalable codes exist for solving integer-order partial differential equations (PDEs), the same is not true for fractional order PDEs. Therefore, there is a need for highly scalable numerical methods and codes for solving fractional order PDEs on complex geometries. The key challenge is that most approaches for fractional PDEs have at least quadratic complexity in both storage and compute, and are challenging to scale. We present a scalable framework for solving fractional diffusion equations using the method of eigen-function expansion. This includes a scalable parallel algorithm to efficiently compute the full set of eigenvalues and eigenvectors for a discretized Laplace eigenvalue problem and apply them to construct approximate solutions to fractional order model problems. We demonstrate the efficacy of our methods by performing strong and weak scalability tests using complex geometries on TACC's Frontera compute cluster. We also show that our approach compares favorably against existing dense and sparse solvers. In our largest solve, we estimated half a million eigenpairs using 28,672 cores.
Ring-opening carbonyl-olefin metathesis of norbornenes.

A computational and experimental study of the hydrazine-catalyzed ring-opening carbonyl–olefin metathesis of norbornenes is described. Detailed theoretical investigation of the energetic landscape for the full reaction pathway with six different hydrazines revealed several crucial aspects for the design of next-generation hydrazine catalysts. This study indicated that a [2.2.2]-bicyclic hydrazine should offer substantially increased reactivity versus the previously reported [2.2.1]-hydrazine due to a lowered activation barrier for the rate-determining cycloreversion step, a prediction which was verified experimentally. Optimized conditions for both cycloaddition and cycloreversion steps were identified, and a brief substrate scope study for each was conducted. A complication for catalysis was found to be the slow hydrolysis of the ring-opened hydrazonium intermediates, which were shown to suffer from a competitive and irreversible cycloaddition with a second equivalent of norbornene. This problem was overcome by the strategic incorporation of a bridgehead methyl group on the norbornene ring, leading to the first demonstrated catalytic carbonyl–olefin metathesis of norbornene rings.
ZipSeq: barcoding for real-time mapping of single cell transcriptomes.

Spatial transcriptomics seeks to integrate single cell transcriptomic data within the three-dimensional space of multicellular biology. Current methods to correlate a cell’s position with its transcriptome in living tissues have various limitations. We developed an approach, called ‘ZipSeq’, that uses patterned illumination and photocaged oligonucleotides to serially print barcodes (‘zipcodes’) onto live cells in intact tissues, in real time and with an on-the-fly selection of patterns. Using ZipSeq, we mapped gene expression in three settings: in vitro wound healing, live lymph node sections and a live tumor microenvironment. In all cases, we discovered new gene expression patterns associated with histological structures. In the tumor microenvironment, this demonstrated a trajectory of myeloid and T cell differentiation from the periphery inward. A combinatorial variation of ZipSeq efficiently scales in the number of regions defined, providing a pathway for complete mapping of live tissues, subsequent to real-time imaging or perturbation. ZipSeq uses patterned illumination and photocaged oligonucleotides to serially print barcodes onto live, intact tissues for spatially mapping single cell transcriptomes.
Legal and Policy Interventions to Address Social Isolation.

Americans suffer from an epidemic of loneliness stemming from perceived or actual social isolation (SI). Multi-faceted causes warrant novel public health legal responses to reduce significant impacts of SI on physical and mental health.
Stealthy movements and concealed swarms of swimming micro-robots

Here, we show that micro-swimmers can form a concealed swarm through synergistic cooperation in suppressing one another’s disturbing flows. We then demonstrate how such a concealed swarm can actively gather around a favorite spot, point toward a target, or track a desired trajectory in space, while minimally disturbing the ambient fluid. Our findings provide a clear road map to control and lead flocks of swimming micro-robots in stealth vs fast modes, tuned through their active collaboration in minimally disturbing the host medium.
Addressing the Large Standard Error of Traditional CBM-R: Estimating the Conditional Standard Error of a Model-Based Estimate of CBM-R:

Curriculum-based measurement of oral reading fluency (CBM-R) is widely used across the country as a quick measure of reading proficiency that also serves as a good predictor of comprehension and ov...
Learning through the Grapevine: The Impact of Message Mutation, Transmission Failure, and Deliberate Bias

We examine how well people learn when information is noisily relayed from person to person, subject to: dropping, mutation, and deliberate manipulation. This allows us to explore how communication platforms can improve learning without censoring or even examining messages, but purely by limiting the number of times a message can be relayed or the number of people to whom someone can forward a message. In particular, we analyze learning as a function of the network depth (length of relay chains) and breadth (how many chains a person has access to). Noise builds up as depth increases and so learning requires greater breadth, which we show to be characterized via a sharp threshold above which the receiver learns fully and below which the receiver learns nothing. Moreover, we show that small uncertainty about the rates of mis- and dis-information make learning from long chains of messages impossible. Optimizing learning requires either limiting depth (by controlling how many times a message can be forwarded), or if that is not possible then limiting breadth (by capping the number of people to whom someone can forward a message). Although limiting breadth decreases the overall amount of information a learner has access to, it increases the relative fraction of messages that are coming from nearby compared to far away in the network, and thus increases the signal to noise ratio. Such policies do not require the ability to fact-check, respect privacy, increase the fraction of true to false messages, and have been implemented by communication platforms. Finally, we extend our model to study learning from dropping rates (e.g., people are more likely to pass messages with one conclusion than another). We find that as the distance to primary sources grows, all learning comes from either the total number of messages received or from the content of received messages, but the learner does not need to pay attention to both. Full paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3269543
Quadratic Gorenstein algebras with many surprising properties

Let k be a field of characteristic 0. Using the method of idealization, we show that there is a non-Koszul, quadratic, Artinian, Gorenstein, standard graded k-algebra of regularity 3 and codimension 8, answering a question of Mastroeni, Schenck, and Stillman. We also show that this example is minimal in the sense that no other idealization that is non-Koszul, quadratic, Artinian, Gorenstein algebra, with regularity 3 has smaller codimension. We also construct an infinite family of graded, quadratic, Artinian, Gorenstein algebras $$A_m$$ , indexed by an integer $$m \ge 2$$ , with the following properties: (1) there are minimal first syzygies of the defining ideal in degree $$m+2$$ , (2) for $$m \ge 3$$ , $$A_m$$ is not Koszul, (3) for $$m \ge 7$$ , the Hilbert function of $$A_m$$ is not unimodal, and thus (4) for $$m \ge 7$$ , $$A_m$$ does not satisfy the weak or strong Lefschetz properties. In particular, the subadditivity property fails for quadratic Gorenstein ideals. Finally, we show that the idealization of a construction of Roos yields non-Koszul quadratic Gorenstein algebras such that the residue field k has a linear resolution for precisely $$\alpha $$ steps for any integer $$\alpha \ge 2$$ . Thus there is no finite test for the Koszul property even for quadratic Gorenstein algebras.
Surface and Temporal Biosignatures

Recent discoveries of potentially habitable exoplanets have ignited the prospect of spectroscopic investigations of exoplanet surfaces and atmospheres for signs of life. This chapter provides an overview of potential surface and temporal exoplanet biosignatures, reviewing Earth analogues and proposed applications based on observations and models. The vegetation red-edge (VRE) remains the most well-studied surface biosignature. Extensions of the VRE, spectral "edges" produced in part by photosynthetic or nonphotosynthetic pigments, may likewise present potential evidence of life. Polarization signatures have the capacity to discriminate between biotic and abiotic "edge" features in the face of false positives from band-gap generating material. Temporal biosignatures -- modulations in measurable quantities such as gas abundances (e.g., CO2), surface features, or emission of light (e.g., fluorescence, bioluminescence) that can be directly linked to the actions of a biosphere -- are in general less well studied than surface or gaseous biosignatures. However, remote observations of Earth's biosphere nonetheless provide proofs of concept for these techniques and are reviewed here. Surface and temporal biosignatures provide complementary information to gaseous biosignatures, and while likely more challenging to observe, would contribute information inaccessible from study of the time-averaged atmospheric composition alone.
Nanocluster Growth and Coalescence Modulated by Ligands

We describe a model of nanocluster formation that incorporates competition between ligand adsorption and nanocluster growth. Growth occurs through the addition of a metal–ligand complex and coalesc...
Element content and distribution has limited, tolerance metric dependent, impact on salinity tolerance in cultivated sunflower (Helianthus annuus).

Disruption of ion homeostasis is a major component of salinity stress's effect on crop yield. In cultivated sunflower prior work revealed a negative relationship between vigor and salinity tolerance. Here, we determined the association of elemental content/distribution traits with salinity tolerance, both with and without taking vigor (biomass in control treatment) into account. We grew seedlings of 12 Helianthus annuus genotypes in two treatments (0, 100 mM NaCl). Plants were measured for biomass (+allocation), and element content (Na, P, K, Ca, Mg, S, Fe, B, Mn, Cu, Zn) in leaves (young and mature), stem, and roots. Genotype tolerance was assessed as both proportional decline of biomass and as expectation deviation (deviation from the observed relationship between vigor and proportional decline in biomass). Genotype rankings on these metrics were not the same. Elemental content and allocation/distribution were highly correlated both at the plant and organ level. Suggestive associations between tolerance and elemental traits were fewer and weaker than expected and differed by tolerance metric. Given the highly correlated nature of elemental content, it remains difficult to pinpoint specific traits underpinning tolerance. Results do show that taking vigor into account is important when seeking to determining traits that can be targeted to increase tolerance independent of vigor, and that the multivariate nature of associated traits should additionally be considered.
Direct atomistic modeling of solute drag by moving grain boundaries

Abstract We show that molecular dynamics (MD) simulations are capable of reproducing the drag of solute segregation atmospheres by moving grain boundaries (GBs). Although lattice diffusion is frozen out on the MD timescale, the accelerated GB diffusion provides enough atomic mobility to allow the segregated atoms to follow the moving GB. This finding opens the possibility of studying the solute drag effect with atomic precision using the MD approach. We demonstrate that a moving GB activates diffusion and alters the short-range order in the lattice regions swept during its motion. It is also shown that a moving GB drags an atmosphere of non-equilibrium vacancies, which accelerate diffusion in surrounding lattice regions.
Quantum-Secure Microgrid

Existing microgrid communication relies on classical public key systems, which are vulnerable to attacks from quantum computers. This paper uses quantum key distribution (QKD) to solve these quantum-era microgrid challenges. Specifically, this paper makes the following novel contributions: 1) it devises a novel QKD simulator capable of simulating QKD protocols; 2) it offers a QKD-based microgrid communication architecture for microgrids; 3) it shows how to build a quantum-secure microgrid testbed in an RTDS environment; 4) it develops a key pool sharing (KPS) strategy to improve the cyberattack resilience of the QKD-based microgrid; and 5) it analyzes the impacts of critical QKD parameters with the testbed. Test results provide insightful resources for building a quantum-secure microgrid.
Atlasing of Assembly Landscapes using Distance Geometry and Graph Rigidity.

This paper describes a novel geometric methodology for analyzing free-energy and kinetics of assembly driven by short-range pair-potentials in an implicit solvent, and provides a proof-of-concept i...
Regulation of T cell receptor signaling by protein acyltransferase DHHC21.

S-acylation reversible-post-translational lipidation of cysteine residues-is emerging as an important regulatory mechanism in T cell signaling. Dynamic S-acylation is critical for protein recruitment into the T cell receptor complex and initiation of the subsequent signaling cascade. However, the enzymatic control of protein S-acylation in T cells remains poorly understood. Here, we report a previously uncharacterized role of DHHC21, a member of the mammalian family of DHHC protein acyltransferases, in regulation of the T cell receptor pathway. We found that loss of DHHC21 prevented S-acylation of key T cell signaling proteins, resulting in disruption of the early signaling events and suppressed expression of T cell activation markers. Furthermore, downregulation of DHHC21 prevented activation and differentiation of naive T cells into effector subtypes. Together, our study provides the first direct evidence that DHHC protein acyltransferases can play an essential role in regulation of T cell-mediated immunity.
Minimizing Localized Ratio Cut Objectives in Hypergraphs

Hypergraphs are a useful abstraction for modeling multiway relationships in data, and hypergraph clustering is the task of detecting groups of closely related nodes in such data.Graph clustering has been studied extensively, and there are numerous methods for detecting small, localized clusters without having to explore an entire input graph. However, there are only a few specialized approaches for localized clustering in hypergraphs. Here we present a framework for local hypergraph clustering based on minimizing localized ratio cut objectives. Our framework takes an input set of reference nodes in a hypergraph and solves a sequence of hypergraph minimum s-t cut problems in order to identify a nearby well-connected cluster of nodes that overlaps substantially with the input set. Our methods extend graph-based techniques but are significantly more general and have new output quality guarantees. First, our methods can minimize new generalized notions of hypergraph cuts, which depend on specific configurations of nodes within each hyperedge, rather than just on the number of cut hyperedges. Second, our framework has several attractive theoretical properties in terms of output cluster quality. Most importantly, our algorithm is strongly-local, meaning that its runtime depends only on the size of the input set, and does not need to explore the entire hypergraph to find good local clusters. We use our methodology to effectively identify clusters in hypergraphs of real-world data with millions of nodes, millions of hyperedges, and large average hyperedge size with runtimes ranging between a few seconds and a few minutes.
Space-time collocation method: Loop quantum Hamiltonian constraints

A space-time collocation method (STCM) using asymptotically-constant basis functions is proposed and applied to the quantum Hamiltonian constraint for a loop-quantized treatment of the Schwarzschil...
Ionospheric backscatter observations at Millstone Hill

Abstract Studies of the electron density, electron and ion temperatures in the F-region have been made by means of ground-based radar observations at the Millstone Hill radar observatory. A 70 m parabolic antenna directed vertically and a 2.5 MW pulse radar operating at 440 Mc s have been employed for these measurements which were conducted for periods of 30 hr at approximately weekly intervals. The paper presents the results of observations extending over a period of one year from February 1963 to January 1964. The diurnal and seasonal variations of the density, electron and ion temperatures are discussed in some detail. It was found that the ratio T e T i achieved a maximum value ~2.0–2.6 at a height of about 300 km soon after dawn irrespective of the season. There was little change in the height dependence in this ratio throughout the daylight hours, and at sunset the ratio fell with a time constant of the order of an hour. At night T e T i was on occasion close to unity, but more often there remained a significant difference in the temperatures at all heights. The ion temperature was found to increase with height at all times, but at night above 500 km this may be due in part to the presence of an unknown amount of He+ ions which considerably affects the interpretation of the signal spectra. The electron temperatures were largely independent of height above about 300 km. Evidence is presented of ionospheric heating during magnetically disturbed conditions, but it is shown that this is only of great importance at night. The results for the electron temperature are compared with results obtained using the Explorer VIII, Explorer XVII and Ariel satellites. Good agreement is obtained with the Explorer XVII results and it appears that the Ariel values are underestimates.
Polyadenylation of Histone H3.1 mRNA Promotes Cell Transformation by Displacing H3.3 from Gene Regulatory Elements.

Summary Replication-dependent canonical histone messenger RNAs (mRNAs) do not terminate with a poly(A) tail at the 3′ end. We previously demonstrated that exposure to arsenic, an environmental carcinogen, induces polyadenylation of canonical histone H3.1 mRNA, causing transformation of human cells in vitro. Here we report that polyadenylation of H3.1 mRNA increases H3.1 protein, resulting in displacement of histone variant H3.3 at active promoters, enhancers, and insulator regions, leading to transcriptional deregulation, G2/M cell-cycle arrest, chromosome aneuploidy, and aberrations. In support of these observations, knocking down the expression of H3.3 induced cell transformation, whereas ectopic expression of H3.3 attenuated arsenic-induced cell transformation. Notably, arsenic exposure also resulted in displacement of H3.3 from active promoters, enhancers, and insulator regions. These data suggest that H3.3 displacement might be central to carcinogenesis caused by polyadenylation of H3.1 mRNA upon arsenic exposure. Our findings illustrate the importance of proper histone stoichiometry in maintaining genome integrity.
Higher-derivative corrections to entropy and the weak gravity conjecture in Anti-de Sitter space

We compute the four-derivative corrections to the geometry, extremality bound, and thermodynamic quantities of AdS-Reissner-Nordstrom black holes for general dimensions and horizon geometries. We confirm the universal relationship between the extremality shift at fixed charge and the shift of the microcanonical entropy, and discuss the consequences of this relation for the Weak Gravity Conjecture in AdS. The thermodynamic corrections are calculated using two different methods: first by explicitly solving the higher-derivative equations of motion and second, by evaluating the higher-derivative Euclidean on-shell action on the leading-order solution. In both cases we find agreement, up to the addition of a Casimir energy in odd dimensions. We derive the bounds on the four-derivative Wilson coefficients implied by the conjectured positivity of the leading corrections to the microcanonical entropy of thermodynamically stable black holes. These include the requirement that the coefficient of Riemann-squared is positive, meaning that the positivity of the entropy shift is related to the condition that c − a is positive in the dual CFT. We discuss implications for the deviation of η/s from its universal value and a potential lower bound.
Networks of Influence: Implementing Politically Sustainable Multinational Stakeholder Strategies

In a bid to gain stakeholder support for their operations, multinational firms operating in politically uncertain environments often inappropriately apply a rational financial approach to a sociopo...
DNA methylation mediates the effect of cocaine use on HIV severity

Cocaine use accelerates human immunodeficiency virus (HIV) progression and worsens HIV outcomes. We assessed whether DNA methylation in blood mediates the association between cocaine use and HIV severity in a veteran population. We analyzed 1435 HIV-positive participants from the Veterans Aging Cohort Study Biomarker Cohort (VACS-BC). HIV severity was measured by the Veteran Aging Cohort Study (VACS) index. We assessed the effect of cocaine use on VACS index and mortality among the HIV-positive participants. We selected candidate mediators that were associated with both persistent cocaine use and VACS index by epigenome-wide association (EWA) scans at a liberal p value cutoff of 0.001. Mediation analysis of the candidate CpG sites between cocaine’s effect and the VACS index was conducted, and the joint mediation effect of multiple CpGs was estimated. A two-step epigenetic Mendelian randomization (MR) analysis was conducted as validation. More frequent cocaine use was significantly associated with a higher VACS index (β = 1.00, p = 2.7E−04), and cocaine use increased the risk of 10-year mortality (hazard ratio = 1.10, p = 0.011) with adjustment for confounding factors. Fifteen candidate mediator CpGs were selected from the EWA scan. Twelve of these CpGs showed significant mediation effects, with each explaining 11.3–29.5% of the variation. The mediation effects for 3 of the 12 CpGs were validated by the two-step epigenetic MR analysis. The joint mediation effect of the 12 CpGs accounted for 47.2% of cocaine’s effect on HIV severity. Genes harboring these 12 CpGs are involved in the antiviral response (IFIT3, IFITM1, NLRC5, PLSCR1, PARP9) and HIV progression (CX3CR1, MX1). We identified 12 DNA methylation CpG sites that appear to play a mediation role in the association between cocaine use and HIV severity.