cssi/SciSciGPT-SciSciNet huggingface dataset:
Understanding the chemistry of cation leaching in illite/water interfacial system using reactive molecular dynamics simulations and hydrothermal experiments

Abstract Despite long standing pursuit, fundamental questions concerning the chemical pathways of leaching of ions in minerals, a phenomenon crucial to energy extraction, hydrometallurgy, metal recovery, and agriculture remain unanswered. Here we use large-scale ReaxFF reactive molecular dynamics (MD) simulations in combination with hydrothermal experiments to understand the chemistry of leaching in illite exposed to aqueous environment. Our simulations show that potassium counterions leach out into the solution much earlier and in higher concentration when compared with aluminum and silicon, which form the structural network of illite. Upon analyzing the chemical pathway from the trajectory of MD simulations, water molecules supply protons near the illite surface that binds with the non-bridging oxygen (NBO) of the Al-O-Si linkage forming an [Al-O-Si]—H transition state that later converts to silanol group upon Al-O bond dissociation. Proton addition also weakens the interlayer K-O bonds, resulting in the diffusion of K+ ions to illite surface, where they combine with the hydroxyl group formed from water dissociation, to form KOH molecules. KOH molecules diffuse out reactively to bulk water via proton exchange mechanism. Furthermore, we also find that continued protonation results in the formation of Al(OH)3 and Si(OH)4 groups predominantly at the surface, which diffuse out into water resulting in the leaching of Al and Si cations. We also estimated the kinetics of surface reactivity from the MD simulations and explored its effectiveness as a surrogate model for leaching kinetics. However, surface reaction kinetics and experimentally measured leaching kinetics seemed to be off by several orders of magnitude. We also analyzed the effects of leaching on structural distortion and found that more than 20% leaching is required for a notable structural distortion in illite crystal.
All-Optical Electrophysiology Reveals the Role of Lateral Inhibition in Sensory Processing in Cortical Layer 1.

Summary Cortical layer 1 (L1) interneurons have been proposed as a hub for attentional modulation of underlying cortex, but the transformations that this circuit implements are not known. We combined genetically targeted voltage imaging with optogenetic activation and silencing to study the mechanisms underlying sensory processing in mouse barrel cortex L1. Whisker stimuli evoked precisely timed single spikes in L1 interneurons, followed by strong lateral inhibition. A mild aversive stimulus activated cholinergic inputs and evoked a bimodal distribution of spiking responses in L1. A simple conductance-based model that only contained lateral inhibition within L1 recapitulated the sensory responses and the winner-takes-all cholinergic responses, and the model correctly predicted that the network would function as a spatial and temporal high-pass filter for excitatory inputs. Our results demonstrate that all-optical electrophysiology can reveal basic principles of neural circuit function in vivo and suggest an intuitive picture for how L1 transforms sensory and modulatory inputs. Video Abstract Download : Download video (36MB)
Remarks On Essential Codimension

We look for generalizations of the Brown–Douglas–Fillmore essential codimension result, leading to interesting local uniqueness theorems in KK theory. We also study the structure of Paschke dual algebras.
Exact identification of circuits using fixed points of amplification functions

A technique for exactly identifying certain classes of read-once Boolean formulas is introduced. The method is based on sampling the input-output behavior of the target formula on a probability distribution which is determined by the fixed point of the formula's amplification function (defined as the probability that a 1 is output by the formula when each input bit is 1 independently with probability p). By performing various statistical tests on easily sampled variants of the fixed-point distribution, it is possible to infer efficiently all structural information about any logarithmic-depth target family (with high probability). The results are used to prove the existence of short universal identification sequences for large classes of formulas. Extensions of the algorithms to handle high rates of noise and to learn formulas of unbounded depth in L.G. Valiant's (1984) model with respect to specific distributions are described. >
SQUARE-INTEGRABILITY OF THE MIRZAKHANI FUNCTION AND STATISTICS OF SIMPLE CLOSED GEODESICS ON HYPERBOLIC SURFACES

Given integers $g,n \geq 0$ satisfying $2-2g-n < 0$, let $\mathcal{M}_{g,n}$ be the moduli space of connected, oriented, complete, finite area hyperbolic surfaces of genus $g$ with $n$ cusps. We study the global behavior of the Mirzakhani function $B \colon \mathcal{M}_{g,n} \to \mathbf{R}_{\geq 0}$ which assigns to $X \in \mathcal{M}_{g,n}$ the Thurston measure of the set of measured geodesic laminations on $X$ of hyperbolic length $\leq 1$. We improve bounds of Mirzakhani describing the behavior of this function near the cusp of $\mathcal{M}_{g,n}$ and deduce that $B$ is square-integrable with respect to the Weil-Petersson volume form. We relate this knowledge of $B$ to statistics of counting problems for simple closed hyperbolic geodesics.
Cohesin Removal Reprograms Gene Expression upon Mitotic Entry.

As cells enter mitosis, the genome is restructured to facilitate chromosome segregation, accompanied by dramatic changes in gene expression. However, the mechanisms that underlie mitotic transcriptional regulation are unclear. In contrast to transcribed genes, centromere regions retain transcriptionally active RNA polymerase II (Pol II) in mitosis. Here, we demonstrate that chromatin-bound cohesin is necessary to retain elongating Pol II at centromeres. We find that WAPL-mediated removal of cohesin from chromosome arms during prophase is required for the dissociation of Pol II and nascent transcripts, and failure of this process dramatically alters mitotic gene expression. Removal of cohesin/Pol II from chromosome arms in prophase is important for accurate chromosome segregation and normal activation of gene expression in G1. We propose that prophase cohesin removal is a key step in reprogramming gene expression as cells transition from G2 through mitosis to G1.
TÓPICOS DE LINGUÍSTICA E LITERATURA NO LETRAMENTO E NA FORMAÇÃO DE ALFABETIZADORES

O presente artigo tem como titulo “Topicos de Linguistica e Literatura no letramento e na formacao de alfabetizadores” e trata de conhecimentos da Linguistica e da Literatura que precisam de ressignificacao na formacao academica inicial e continua de professores alfabetizadores. O intuito deste trabalho nao e dissertar a respeito de teorias e correntes da Linguistica acerca do letramento inicial ou discorrer sobre fonologia da lingua portuguesa, nem argumentar sobre Teoria, Critica ou Historia da Literatura, mas e provocar reflexao e discussao a respeito de topicos do arcabouco teorico da Linguistica e da Literatura ausentes ou mal compreendidos na formacao de pedagogos alfabetizadores e professores de Lingua Portuguesa. Dentro dessa proposta, e imperativo fazer consideracoes breves tambem sobre mudancas paradigmaticas na formacao de professores e sobre a implicacao dos problemas analisados no percurso escolar dos estudantes. Sob o metodo da pesquisa bibliografica, com cruzamento de fontes dos ultimos dez anos e outras mais antigas, e interpretacao de conteudo, os argumentos foram construidos no sentido de retomar a importância do tema paralelamente a outros predominantes nos discursos academicos. Araujo e Oliveira (2008) e Pimenta (1996), dentre outros, formam as referencias de embasamento teorico e contribuem para o resultado da pesquisa: as fontes que tratam de letramento dao importância aos generos literarios e conhecimentos linguisticos, em destaque a base fonemica da lingua portuguesa, mas as fontes que tratam de formacao de professores permanecem com enfase em outros temas. Logo, sao necessarias pesquisas de campo na realidade escolar, da pratica a teoria e da teoria a pratica, que promovam novas aproximacoes e reflexoes a respeito dos conhecimentos elencados no trabalho, a fim de mitigar problemas estruturais na alfabetizacao.
Analyzing Large Receptive Field Convolutional Networks for Distant Speech Recognition

Despite significant efforts over the last few years to build a robust automatic speech recognition (ASR) system for different acoustic settings, the performance of the current state-of-the-art technologies significantly degrades in noisy reverberant environments. Convolutional Neural Networks (CNNs) have been successfully used to achieve substantial improvements in many speech processing applications including distant speech recognition (DSR). However, standard CNN architectures were not efficient in capturing long-term speech dynamics, which are essential in the design of a robust DSR system. In the present study, we address this issue by investigating variants of large receptive field CNNs (LRF-CNNs) which include deeply recursive networks, dilated convolutional neural networks, and stacked hourglass networks. To compare the efficacy of the aforementioned architectures with the standard CNN for Wall Street Journal (WSJ) corpus, we use a hybrid DNN-HMM based speech recognition system. We extend the study to evaluate the system performances for distant speech simulated using realistic room impulse responses (RIRs). Our experiments show that with fixed number of parameters across all architectures, the large receptive field networks show consistent improvements over the standard CNNs for distant speech. Amongst the explored LRF-CNNs, stacked hourglass network has shown improvements with a 8.9% relative reduction in word error rate (WER) and 10.7% relative improvement in frame accuracy compared to the standard CNNs for distant simulated speech signals.
Characterizing functional relationships between anthropogenic and biological sounds: a western New York state soundscape case study

Roads are a widespread feature of landscapes worldwide, and road traffic sound potentially makes nearby habitat unsuitable for acoustically communicating organisms. It is important to understand the influence of roads at the soundscape level to mitigate negative impacts of road sound on individual species as well as subsequent effects on the surrounding landscape. We seek to characterize the relationship between anthropogenic and biological sounds in western New York and assess the extent to which available traffic data explains variability in anthropogenic noise. Recordings were obtained in the spring of 2016 at 18 sites throughout western New York. We used the Welch Power Spectral Density (PSD) at low frequencies (0.5–2 kHz) to represent anthropogenic noise and PSD values at higher frequencies (2–11 kHz) to represent biological sound. Relationships were modeled using a novel two-stage hierarchical Bayesian model utilizing beta regression and basis splines. Model results and map predictions illustrate that anthropogenic noise and biological sound have an inverse relationship, and anthropogenic noise is greatest in close proximity to high traffic volume roads. The predictions have large uncertainty, resulting from the temporal coarseness of public road data used as a proxy for traffic sound. Results suggest that finer temporal resolution traffic sound data, such as crowd-sourced time-indexed traffic data from geographic positioning systems, might better account for observed temporal changes in the soundscape. The use of such data, in combination with the proposed modeling framework, could have important implications for the development of sound management policies.
Assessing soundscape disturbance through hierarchical models and acoustic indices: a case study on a shelterwood logged northern Michigan forest

Abstract Assessing the effects of anthropogenic disturbances on wildlife and natural resources is a necessary conservation task. The soundscape is a critical habitat component for acoustically communicating organisms, but the use of the soundscape as a tool for assessing disturbance impacts has been relatively unexplored until recently. Here we present a broad modeling framework for assessing disturbance impacts on soundscapes, which we apply to quantify the influence of a shelterwood logging on soundscapes in northern Michigan. Our modeling approach can be broadly applied to assess anthropogenic disturbance impacts on soundscapes. The approach accommodates inherent differences in control and treatment sites to improve inference about treatment effects, while also accounting for extraneous variables (e.g., rain) that influence acoustic indices. Recordings were obtained at 13 sites before and after a shelterwood logging. Four sites were in the logging region and nine sites served as control recordings outside the logging region. We quantify the soundscapes using common acoustic indices (Normalized Difference Soundscape Index (NDSI), Acoustic Entropy (H), Acoustic Complexity Index (ACI), Acoustic Evenness Index (AEI)) and Welch Power Spectral Density (PSD) values. We build two hierarchical Bayesian models to quantify the changes in the soundscape over the study period. Our analysis reveals no long-lasting effects of the shelterwood logging on the soundscape as measured by the NDSI, but analysis of H, AEI, and PSD suggest changes in the evenness of sounds across the frequency spectrum, indicating a potential shift in the avian species communicating in the soundscapes as a result of the logging. Further, our analysis confirms previous findings that the ACI does not accurately reflect changes in landscape configuration. Multiple model validation techniques (i.e., comparison of parameter estimates and the widely applicable information criterion (WAIC)) reveal our proposed hierarchical Bayesian models outperform more simple models used for hypothesis testing. Acoustic recordings, in conjunction with this modeling framework, can deliver cost efficient assessment of disturbance impacts on the landscape and underlying biodiversity as represented through the soundscape.
Relative Entropy for von Neumann Subalgebras

We revisit the connection between index and relative entropy for an inclusion of finite von Neumann algebras. We observe that the Pimsner–Popa index connects to sandwiched p-Renyi relative entropy ...
Big Fish in a Small Pond: Institutional Trading of Penny Stocks

There has been a steady increase in institutional ownership of penny stocks over the past decades. Nevertheless, we show that penny stocks bought by institutional investors significantly underperfo...
Probabilistic Detection and Estimation of Conic Sections From Noisy Data

Inferring unknown conic sections on the basis of noisy data is a challenging problem with applications in computer vision. A major limitation of the currently available methods for conic sections i...
Virial Relations in Density Embedding

The accuracy of charge-transfer excitation energies, solvatochromic shifts and other environmental effects calculated via various density embedding techniques depend critically on the approximations employed for the non-additive non-interacting kinetic energy functional, $T_{\scriptscriptstyle\rm s}^{\scriptscriptstyle\rm nad}[n]$. Approximating this functional remains an important challenge in electronic structure theory. To assist in the development and testing of approximations for $T_{\scriptscriptstyle\rm s}^{\scriptscriptstyle\rm nad}[n]$, we derive two virial relations for fragments in molecules. These establish separate connections between the non-additive kinetic energies of the non-interacting and interacting systems of electrons, and quantities such as the electron-nuclear attraction forces, the partition (or embedding) energy and potential, and the Kohn-Sham potentials of the system and its parts. We numerically verify both relations on diatomic molecules.
Optimal Tolling for Heterogeneous Traffic Networks with Mixed Autonomy

When people pick routes to minimize their travel time, the total experienced delay, or social cost, may be significantly greater than if people followed routes assigned to them by a social planner. This effect is accentuated when human drivers share roads with autonomous vehicles. When routed optimally, autonomous vehicles can make traffic networks more efficient, but when acting selfishly, the introduction of autonomous vehicles can actually worsen congestion. We seek to mitigate this effect by influencing routing choices via tolling. We consider a network of parallel roads with affine latency functions that are heterogeneous, meaning that the increase in capacity due to to the presence of autonomous vehicles may vary from road to road. We show that if human drivers and autonomous users have the same tolls, the social cost may be arbitrarily worse than when optimally routed. We then prove qualities of the optimal routing and use them to design tolls that are guaranteed to minimize social cost at equilibrium. To the best of our knowledge, this is the first tolling scheme that yields a unique socially optimal equilibrium for parallel heterogeneous network with affine latency functions.
Rethinking global carbon storage potential of trees. A comment on Bastin et al. (2019)

Bastin et al. 2019 use two flawed assumptions: 1) that the area suitable for restoration does not contain any carbon currently, and 2) that soil organic carbon (SOC) from increased canopy cover will accumulate quickly enough to mitigate anthropogenic carbon emissions. We re-evaluated the potential carbon storage worldwide using empirical relationships of tree cover and carbon. We use global datasets of tree cover, soil organic carbon, and above ground biomass to estimate the empirical relationships of tree cover and carbon stock storage. A more realistic range of global carbon storage potential is between 71.7 and 75.7 GtC globally, with a large uncertainty associated with SOC. This is less than half of the original 205 GtC estimate. The potential global carbon storage of restored forests is much less than that estimated by Bastin et al. 2019. While we agree on the value of assessing global reforestation potential, we suggest caution in considering it the most effective strategy to mitigate anthropogenic emissions. A preprint version of this article was published on 13 August 2019 at https://doi.org/10.1101/730325
A Model Problem for Nematic-Isotropic Transitions with Highly Disparate Elastic Constants

Continuing the program initiated in Golovaty et al. (SIAM J Math Anal 51(1):276–320, 2018), we analyze a model problem based on highly disparate elastic constants that we propose in order to understand corners and cusps that form on the boundary between the nematic and isotropic phases in a liquid crystal. For a bounded planar domain $$\Omega $$ we investigate the $$\varepsilon \rightarrow 0$$ asymptotics of the variational problem $$\begin{aligned} \inf \displaystyle \frac{1}{2}\int _\Omega \left( \frac{1}{\varepsilon } W(u)+\varepsilon |\nabla u|^2 + L_\varepsilon (\mathrm {div}\,u)^2 \right) \,\hbox {d}x \end{aligned}$$within various parameter regimes for $$L_\varepsilon > 0.$$ Here $$u:\Omega \rightarrow \mathbb {R}^2$$ and W is a potential vanishing on the unit circle and at the origin. When $$\varepsilon \ll L_\varepsilon \rightarrow 0$$, we show that these functionals $$\Gamma $$-converge to a constant multiple of the perimeter of the phase boundary and the divergence penalty is not felt. However, when $$L_\varepsilon \equiv L > 0$$, we find that a tangency requirement along the phase boundary for competitors in the conjectured $$\Gamma $$-limit becomes a mechanism for development of singularities. We establish criticality conditions for this limit and under a non-degeneracy assumption on the potential we prove the compactness of energy bounded sequences in $$L^2$$. The role played by this tangency condition on the formation of interfacial singularities is investigated through several examples: each of these examples involves analytically rigorous reasoning motivated by numerical experiments. We argue that generically, “wall” singularities between $$\mathbb {S}^1$$-valued states of the kind analyzed in Golovaty et al. (SIAM J Math Anal 51(1):276–320, 2018) are expected near the defects along the phase boundary.
Tableau stabilization and rectangular tableaux fixed by promotion powers

We introduce tableau stabilization, a new phenomenon and statistic on Young tableaux based on jeu de taquin. We investigate bounds for tableau stabilization, the shape of stabilized tableaux, and tableau stabilization as a permutation statistic. We apply tableau stabilization to construct the sufficiently large rectangular tableaux fixed by powers of promotion, which were counted by Brendon Rhoades via the cyclic sieving phenomenon [(Rhoades in J Combin Theory Ser A 117(1):38–76, 2010), Theorem 1.3].
Exploiting Fast Decaying and Locality in Multi-Agent MDP with Tree Dependence Structure

This paper considers a multi-agent Markov Decision Process (MDP), where there are n agents and each agent i is associated with a state s i and action a i taking values from a finite set. Though the global state space size and action space size are exponential in n, we impose local dependence structures and focus on local policies that only depend on local states, and we propose a method that finds nearly optimal local policies in polynomial time (in n) when the dependence structure is a one directional tree. The algorithm builds on approximated reward functions which are evaluated using locally truncated Markov process. Further, under some special conditions, we prove that the gap between the approximated reward function and the true reward function is decaying exponentially fast as the length of the truncated Markov process gets longer. The intuition behind this is that under some assumptions, the effect of agent interactions decays exponentially in the distance between agents, which we term "fast decaying property". Results in this paper are our preliminary steps towards designing efficient reinforcement learning algorithms with optimality guarantees in large multi-agent MDP problems whose state (action) space size is exponentially large in n.
Resolving single-cell heterogeneity from hundreds of thousands of cells through sequential hybrid clustering and NMF.

Motivation The rapid proliferation of single-cell RNA-sequencing (scRNA-Seq) technologies has spurred the development of diverse computational approaches to detect transcriptionally coherent populations. While the complexity of the algorithms for detecting heterogeneity has increased, most require significant user-tuning, are heavily reliant on dimension reduction techniques and are not scalable to ultra-large datasets. We previously described a multi-step algorithm, Iterative Clustering and Guide-gene Selection (ICGS), which applies intra-gene correlation and hybrid clustering to uniquely resolve novel transcriptionally coherent cell populations from an intuitive graphical user interface. Results We describe a new iteration of ICGS that outperforms state-of-the-art scRNA-Seq detection workflows when applied to well-established benchmarks. This approach combines multiple complementary subtype detection methods (HOPACH, sparse non-negative matrix factorization, cluster 'fitness', support vector machine) to resolve rare and common cell-states, while minimizing differences due to donor or batch effects. Using data from multiple cell atlases, we show that the PageRank algorithm effectively downsamples ultra-large scRNA-Seq datasets, without losing extremely rare or transcriptionally similar yet distinct cell types and while recovering novel transcriptionally distinct cell populations. We believe this new approach holds tremendous promise in reproducibly resolving hidden cell populations in complex datasets. Availability and implementation ICGS2 is implemented in Python. The source code and documentation are available at http://altanalyze.org. Supplementary information Supplementary data are available at Bioinformatics online.
Two-sided heterogeneity, endogenous sharing, and international matching markets

This paper develops a multi-country, multi-sector, and multi-factor model of two-sided matching between heterogeneous workers and entrepreneurs in which agents in different countries can form cross-country teams. Sorting, matching, and sharing problems are all considered in a unified framework. Equilibrium is characterized by endogenous sharing rules, which break away from competitive marginal productivity theories of factor returns. I illustrate that a bilateral economic integration agreement can affect the welfare of agents in an unrelated third country, and that a reduction in the cost of sector-specific matching can increase welfare for all agents without conflicts of interest.
When will I get my paper back? A replication study of publication timelines for health professions education research

Biomedical researchers have lamented the lengthy timelines from manuscript submission to publication and highlighted potential detrimental effects on scientific progress and scientists’ careers. In 2015, Himmelstein identified the mean time from manuscript submission to acceptance in biomedicine as approximately 100 days. The length of publication timelines in health professions education (HPE) is currently unknown. This study replicates Himmelstein’s work with a sample of 14 HPE journals published between 2008–2018. Using PubMed, 19,182 article citations were retrieved. Open metadata for each were downloaded, including the date the article was received by the journal, date the authors resubmitted revisions, date the journal accepted the article, and date of entry into PubMed. Journals without publication history metadata were excluded. Publication history data were available for 55% (n = 8) of the journals sampled. The publication histories of 4,735 (25%) articles were analyzed. Mean time from: (1) author submission to journal acceptance was 180.93 days (SD = 103.89), (2) author submission to posting on PubMed was 263.55 days (SD = 157.61), and (3) journal acceptance to posting on PubMed was 83.15 days (SD = 135.72). This study presents publication metadata for journals that openly provide it—a first step towards understanding publication timelines in HPE. Findings confirm the replicability of the original study, and the limited data suggest that, in comparison to biomedical scientists broadly, medical educators may experience longer wait times for article acceptance and publication. Reasons for these delays are currently unknown and deserve further study; such work would be facilitated by increased public access to journal metadata.
Chapter 6 – Assembly of Element Matrices and Vectors and Derivation of System Equations

The assembly of element matrices and vectors, derivation of the system equations, and incorporation of the boundary conditions are considered. Because different local coordinate systems (for different elements) may have been used in deriving the element matrices and vectors, it is necessary to transform the element matrices and vectors of all elements into one global coordinate system. Thus a coordinate transformation matrix is to be derived that relates the nodal unknowns defined in the local coordinate system to those defined in the global coordinate system. By using a scalar characteristic, such as strain energy, that is invariant with the coordinate system, the relations for the element matrices and vectors between the local and global coordinate systems can be derived. The assembly of element matrices and vectors in the global coordinate system to derive the system matrix and system vector based on the requirement of compatibility at element nodes is presented. Three methods are presented for incorporating the boundary conditions. In addition, a penalty method is also outlined for incorporating any general boundary condition in a unified and simple manner. All the methods are presented with illustrative examples.
On the capacity of isolated, curbside bus stops

Abstract The maximal rates that buses can discharge from bus stops are examined. Models were developed to estimate these capacities for curbside stops that are isolated from the effects of traffic signals. The models account for key features of the stops, including their target service levels assigned to them by a transit agency. Among other things, the models predict that adding bus berths to a stop can sometimes return disproportionally high gains in capacity. This and other of our findings are at odds with information furnished in professional handbooks.
X-ray halos around supernova remnants

The results are reported of an Einstein Observatory imaging proportional counter investigation of the X-ray halos of the four brightest young Galactic supernova remnants: the Crab Nebula, Cas A, Tycho's, and Kepler's supernova remnant. It is found that the size, shape, and rough intensity of each of these sources are consistent with the measured properties of the X-ray halos of compact Galactic X-ray sources, and as such are consistent with an origin due solely to the scattering of X-rays by interstellar grains.
Structural Equation Modeling: Nontraditional Alternatives

The most widely known approach for modeling relations between unobservable conceptual variables with multiple observed variable indicators involves using common factors as proxies for the conceptual variables. An important class of alternative approaches uses composites (weighted sums of observed variables) for the same purpose. Among composite-based approaches, partial least squares path modeling is the most widely used. Generalized structured component analysis is a newer composite-based alternative with some advanced capabilities. Regression component analysis uses the parameter estimation tools of factor analysis but renders a solution in terms of composites rather than common factors, thus avoiding factor indeterminacy. Researchers seeking to find models, rather than testing a prespecified model, might employ Tetrad. Confirmatory tetrad analysis allows researchers to test structural equation models without estimating model parameters. Keywords: structural equation modeling; partial least squares path modeling; tetrad; confirmatory tetrad analysis; generalized structured component analysis; regression component analysis; factor indeterminacy; multiple indicator methods; latent variable; proxy
Identifying causal variants by fine mapping across multiple studies.

Genome-Wide Association Studies (GWAS) have successfully identified numerous genetic variants associated with a variety of complex traits in humans. However, most of these associated variants are not causal, and are simply in Linkage Disequilibrium (LD) with the true causal variants. This problem is addressed by statistical “fine mapping” methods, which attempt to prioritize a small subset of variants for further testing while accounting for LD structure [1]. CAVIAR [2] introduced a widely-adopted Bayesian approach that accounted for uncertainty in association statistics using a multivariate normal (MVN) model and allowed for potentially multiple causal SNPs at a locus. There is growing interest in improving fine-mapping by leveraging information from multiple studies. One example of this is trans-ethnic fine mapping, which can significantly improve fine mapping power and resolution by leveraging the distinct LD structures in each population. However, existing methods either assume a single causal SNP at each locus or do not explicitly model heterogeneity, limiting their power.
Companion Cognitive Systems: Design Goals and Some Lessons Learned

The Companion cognitive architecture is designed to support experiments in achieving human-level intelligence. This paper describes seven key design goals of Companions, relating them to properties of human reasoning and learning, and to engineering concerns raised by attempting to build large-scale cognitive systems. We summarize our experiences to date with Companions in two kinds of domains, test taking and game playing. We close by summarizing some of the challenges that remain.
A 0.5 V Sub-Microwatt CMOS Image Sensor With Pulse-Width Modulation Read-Out

Energy minimization is a critical goal in size-constrained wireless sensors. Sensing elements are traditionally power hungry and require special attention in low energy systems. In this work, we study ultra-low power image sensors. In particular, we explore the use of aggressive voltage scaling in CMOS image sensors for applications ranging from retinal prostheses to battlefield monitoring and surveillance. We begin with a discussion of the challenges faced by a traditional 3T active pixel sensor as the supply voltage scales to 0.5 V and below. We then discuss an image sensor with pulse-width modulation read-out that is optimized for 0.5 V operation. A 0.13 μm test-chip with a 128 × 128 pixel array is shown to be functional with V dd as low as 0.45 V with energy consumption of 140 nJ/frame at V dd = 0.5 V (8.5 frames per second) and power consumption of only 700 nW at V dd = 0.5 V (0.5 frames per second). A focus is also placed on quantifying the noise implications of low voltage operation on the test-chip, which has a measured signal-to-noise ratio of 23.4 dB in saturation at V dd = 0.5 V.
Progress in Modeling and Targeting Inner Ear Disorders with Pluripotent Stem Cells.

Sensorineural hearing loss and vestibular dysfunction are caused by damage to neurons and mechanosensitive hair cells, which do not regenerate to any clinically relevant extent in humans. Several protocols have been devised to direct pluripotent stem cells (PSCs) into inner ear hair cells and neurons, which display many properties of their native counterparts. The efficiency, reproducibility, and scalability of these protocols are enhanced by incorporating knowledge of inner ear development. Modeling human diseases in vitro through genetic manipulation of PSCs is already feasible, thereby permitting the elucidation of mechanistic understandings of a wide array of disease etiologies. Early studies on transplantation of PSC-derived otic progenitors have been successful in certain animal models, yet restoration of function and long-term cell survival remain unrealized. Through further research, PSC-based approaches will continue to revolutionize our understanding of inner ear biology and contribute to the development of therapeutic treatments for inner ear disorders.
Prochlorococcus phage ferredoxin: structural characterization and electron transfer to cyanobacterial sulfite reductases.

Marine cyanobacteria are infected by phages whose genomes encode ferredoxin (Fd) electron carriers. These Fds are thought to redirect the energy harvested from light to phage-encoded oxidoreductases that enhance viral fitness, but it is unclear how the biophysical properties and partner specificities of phage Fds relate to those of photosynthetic organisms. Here, results of a bioinformatics analysis using a sequence similarity network revealed that phage Fds are most closely related to cyanobacterial Fds that transfer electrons from photosystems to oxidoreductases involved in nutrient assimilation. Structural analysis of myovirus P-SSM2 Fd (pssm2-Fd), which infects the cyanobacterium Prochlorococcus marinus, revealed high levels of similarity to cyanobacterial Fds (root mean square deviations of ≤0.5 A). Additionally, pssm2-Fd exhibited a low midpoint reduction potential (–336 mV versus a standard hydrogen electrode), similar to other photosynthetic Fds, although it had lower thermostability (Tm = 28 °C) than did many other Fds. When expressed in an Escherichia coli strain deficient in sulfite assimilation, pssm2-Fd complemented bacterial growth when coexpressed with a P. marinus sulfite reductase, revealing that pssm2-Fd can transfer electrons to a host protein involved in nutrient assimilation. The high levels of structural similarity with cyanobacterial Fds and reactivity with a host sulfite reductase suggest that phage Fds evolved to transfer electrons to cyanobacterially encoded oxidoreductases.
Do GRE scores help predict getting a physics Ph.D.? A comment on a paper by Miller et al.

A recent paper in Science Advances by Miller et al. concludes that Graduate Record Examinations (GREs) do not help predict whether physics graduate students will get Ph.D.’s. Here, I argue that the presented analyses reflect collider-like stratification bias, variance inflation by collinearity and range restriction, omission of parts of a needed correlation matrix, a peculiar choice of null hypothesis on subsamples, blurring the distinction between failure to reject a null and accepting a null, and an unusual procedure that inflates the confidence intervals in a figure. Release of results of a model that leaves out stratification by the rank of the graduate program would fix many of the problems.
Deterministic and Randomized Actuator Scheduling With Guaranteed Performance Bounds

In this article, we investigate the problem of actuator selection for linear dynamical systems. We develop a framework to design a sparse actuator schedule for a given large-scale linear system with guaranteed performance bounds using deterministic polynomial-time and randomized approximately linear-time algorithms. First, we introduce systemic controllability metrics for linear dynamical systems that are monotone and homogeneous with respect to the controllability Gramian. We show that several popular and widely used optimization criteria in the literature belong to this class of controllability metrics. Our main result is to provide a polynomial-time actuator schedule that on average selects only a constant number of actuators at each time step, independent of the dimension, to furnish a guaranteed approximation of the controllability metrics in comparison to when all actuators are in use. Our results naturally apply to the dual problem of sensor selection, in which we provide a guaranteed approximation to the observability Gramian. We illustrate the effectiveness of our theoretical findings via several numerical simulations using benchmark examples.
RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network

Spiking Neural Networks (SNNs) have recently attracted significant research interest as the third generation of artificial neural networks that can enable low-power event-driven data analytics. The best performing SNNs for image recognition tasks are obtained by converting a trained Analog Neural Network (ANN), consisting of Rectified Linear Units (ReLU), to SNN composed of integrate-and-fire neurons with "proper" firing thresholds. The converted SNNs typically incur loss in accuracy compared to that provided by the original ANN and require sizable number of inference time-steps to achieve the best accuracy. We find that performance degradation in the converted SNN stems from using "hard reset" spiking neuron that is driven to fixed reset potential once its membrane potential exceeds the firing threshold, leading to information loss during SNN inference. We propose ANN-SNN conversion using "soft reset" spiking neuron model, referred to as Residual Membrane Potential (RMP) spiking neuron, which retains the "residual" membrane potential above threshold at the firing instants. We demonstrate near loss-less ANN-SNN conversion using RMP neurons for VGG-16, ResNet-20, and ResNet-34 SNNs on challenging datasets including CIFAR-10 (93.63% top-1), CIFAR-100 (70.93% top-1), and ImageNet (73.09% top-1 accuracy). Our results also show that RMP-SNN surpasses the best inference accuracy provided by the converted SNN with "hard reset" spiking neurons using 2-8 times fewer inference time-steps across network architectures and datasets.
The Relationship between In-Person Voting and COVID-19: Evidence from the Wisconsin Primary†

Abstract On April 7, 2020, Wisconsin held its presidential primary election, and news reports showed long lines of voters due to fewer polling locations We use county-level variation in voting patterns and weekly county-level COVID test data to examine whether in-person voting increased COVID-19 cases We find a statistically significant association between in-person voting density and the spread of COVID-19 two to three weeks after the election In our main results, a 10% increase in in-person voters per polling location is associated with an 18 4% increase in the COVID-19 positive test rate two to three weeks later
Personalized learning in iSTART: Past modifications and future design

Computer-based learning environments (CBLEs) provide unprecedented opportunities for personalized learning at scale. One such system, iSTART (Interactive Strategy Training for Active Reading and Th...
Delaying Cancer Cases in Urology during COVID-19: Review of the Literature.

Purpose:Coronavirus disease 2019 (COVID-19) is a global pandemic affecting hospital systems and the availability of resources for surgical procedures. Our aim is to provide guidance for urologists ...
Steady state programming of controlled nonlinear systems via deep dynamic mode decomposition.

This paper describes the optimal selection of a control policy to program the steady state of controlled nonlinear systems with hyperbolic fixed points. This work is motivated by the field of synthetic biology, in which saddle points are common (along with limit cycles), and the aim is to program cells to perform both digital and analog computation, though developing genetic digital computation has been the main focus. We frame the analog computing challenge of generating a steady state input-output function inside living cells. To program the steady state, a data-driven approach is taken wherein an approximation of the Koopman operator, identified via deep dynamic mode decomposition, is used to describe the dynamics of the system linearly. The new representation of the dynamics are then used to solve an optimization problem for the input which maximizes a direction in state space. Some added structure on the Koopman operator learning process for controlled systems is given for dynamics that are separable in the state and input. Finally, the methods are demonstrated on simulation examples of an incoherent feedforward loop and a combinatorial promoter system, two common network architectures seen in the field of synthetic biology.
A Bridge to Nowhere? Our Energy Transition and the Natural Gas Pipeline Wars

[enter Abstract BodyThis article canvases the debate surrounding the nation’s increasing reliance on natural gas and the attendant push toward new natural gas pipeline infrastructure. Natural gas is fast becoming the next principal sector our economy must address to arrest greenhouse gas (GHG) emissions in our energy systems. And during the past few years, states, landowners, and environmental organizations have waged what is now being dubbed a “pipeline war.” A “Bridge to Nowhere” portrays this war. It discusses the history of federal natural gas regulation, along with methane’s contribution toward climate change and the Obama and Trump administrations’ opposite responses. It then explores the dominant conversations surrounding new pipeline construction, including whether states enjoy sufficient authority to block new pipeline construction under the Clean Water Act, whether the Federal Energy Regulatory (FERC) impermissibly avoids examining the impact of GHG emissions associated with new pipeline approvals, and finally whether the process that allows new pipeline construction to proceed expeditiously is so “Kafkaesque,” as an August 2019 judged observed, to warrant changes to protect landowners and the environment. Each of these contemporary issues is capturing considerable public attention and increasing judicial interest. The article next probes the often ignored yet fundamental question of whether FERC ought to consider examining the need for new natural gas in the first place before deciding whether to approve new pipeline construction. The article concludes by explaining why FERC can and must do so, and offers a framework and justification for the agency to engage meaningfully in a modern energy policy conversation.
LNBot: A Covert Hybrid Botnet on Bitcoin Lightning Network for Fun and Profit.

While various covert botnets were proposed in the past, they still lack complete anonymization for their servers/botmasters or suffer from slow communication between the botmaster and the bots. In this paper, we propose a new generation hybrid botnet that covertly and efficiently communicates over Bitcoin Lightning Network (LN), called LNBot. LN is a payment channel network operating on top of Bitcoin network for faster Bitcoin transactions with negligible fees. Exploiting various anonymity features of LN, we designed a scalable two-layer botnet which completely anonymize the identity of the botmaster. In the first layer, the botmaster sends commands anonymously to the C&C servers through LN transactions. Specifically, LNBot allows botmaster’s commands to be sent in the form of surreptitious multihop LN payments, where the commands are encoded with ASCII or Huffman encoding to provide covert communications. In the second layer, C&C servers further relay those commands to the bots they control in their mini-botnets to launch any type of attacks to victim machines. We implemented a proof-of-concept on the actual LN and extensively analyzed the delay and cost performance of LNBot. Our analysis show that LNBot achieves better scalibility compared to the other similar blockchain botnets with negligible costs. Finally, we also provide and discuss a list of potential countermeasures to detect LNBot activities and minimize its impacts.
Viral gene drive in herpesviruses.

Gene drives are genetic modifications designed to propagate in a population with high efficiency. Current gene drive strategies rely on sexual reproduction and are thought to be restricted to sexual organisms. Here, we report on a gene drive system that allows the spread of an engineered trait in populations of DNA viruses and, in particular, herpesviruses. We describe the successful transmission of a gene drive sequence between distinct strains of human cytomegalovirus (human herpesvirus 5) and show that gene drive viruses can efficiently target and replace wildtype populations in cell culture experiments. Moreover, by targeting sequences necessary for viral replication, our results indicate that a viral gene drive can be used as a strategy to suppress a viral infection. Taken together, this work offers a proof of principle for the design of a gene drive in viruses. Current gene drive strategies are restricted to sexually reproducing species. Here the authors develop a gene drive in herpesviruses that allows the spread of an engineered trait through a viral population.
Weakly-Supervised Segmentation for Disease Localization in Chest X-Ray Images

Deep Convolutional Neural Networks have proven effective in solving the task of semantic segmentation. However, their efficiency heavily relies on the pixel-level annotations that are expensive to get and often require domain expertise, especially in medical imaging. Weakly supervised semantic segmentation helps to overcome these issues and also provides explainable deep learning models.
One-Bit Compressed Sensing via One-Shot Hard Thresholding

This paper concerns the problem of 1-bit compressed sensing, where the goal is to estimate a sparse signal from a few of its binary measurements. We study a non-convex sparsity-constrained program and present a novel and concise analysis that moves away from the widely used notion of Gaussian width. We show that with high probability a simple algorithm is guaranteed to produce an accurate approximation to the normalized signal of interest under the $\ell_2$-metric. On top of that, we establish an ensemble of new results that address norm estimation, support recovery, and model misspecification. On the computational side, it is shown that the non-convex program can be solved via one-step hard thresholding which is dramatically efficient in terms of time complexity and memory footprint. On the statistical side, it is shown that our estimator enjoys a near-optimal error rate under standard conditions. The theoretical results are substantiated by numerical experiments.