cssi/SciSciGPT-SciSciNet huggingface dataset:
Practical Obstacles to Deploying Active Learning

Active learning (AL) is a widely-used training strategy for maximizing predictive performance subject to a fixed annotation budget. In AL, one iteratively selects training examples for annotation, often those for which the current model is most uncertain (by some measure). The hope is that active sampling leads to better performance than would be achieved under independent and identically distributed (i.i.d.) random samples. While AL has shown promise in retrospective evaluations, these studies often ignore practical obstacles to its use. In this paper, we show that while AL may provide benefits when used with specific models and for particular domains, the benefits of current approaches do not generalize reliably across models and tasks. This is problematic because in practice, one does not have the opportunity to explore and compare alternative AL strategies. Moreover, AL couples the training dataset with the model used to guide its acquisition. We find that subsequently training a successor model with an actively-acquired dataset does not consistently outperform training on i.i.d. sampled data. Our findings raise the question of whether the downsides inherent to AL are worth the modest and inconsistent performance gains it tends to afford.
Dataset Culling: Towards Efficient Training of Distillation-Based Domain Specific Models

Real-time CNN-based object detection models for applications like surveillance can achieve high accuracy but are computationally expensive. Recent works have shown 10 to 100× reduction in computation cost for inference by using domain-specific networks. However, prior works have focused on inference only. If the domain model requires frequent retraining, training costs can pose a significant bottleneck. To address this, we propose Dataset Culling: a pipeline to reduce the size of the dataset for training, based on the prediction difficulty. Images that are easy to classify are filtered out since they contribute little to improving the accuracy. The difficulty is measured using our proposed confidence loss metric with little computational overhead. Dataset Culling is extended to optimize the image resolution to further improve training and inference costs. We develop fixed-angle, long-duration video datasets across several domains, and we show that the dataset size can be culled by a factor of 300× to reduce the total training time by 47× with no accuracy loss or even with slight improvement.1
A novel, dynein-independent mechanism focuses the endoplasmic reticulum around spindle poles in dividing Drosophila spermatocytes

In dividing animal cells the endoplasmic reticulum (ER) concentrates around the poles of the spindle apparatus by associating with astral microtubules (MTs), and this association is essential for proper ER partitioning to progeny cells. The mechanisms that associate the ER with astral MTs are unknown. Because astral MT minus-ends are anchored by centrosomes at spindle poles, we hypothesized that the MT minus-end motor dynein mediates ER concentration around spindle poles. Live in vivo imaging of Drosophila spermatocytes revealed that dynein is required for ER concentration around centrosomes during late interphase. In marked contrast, however, dynein suppression had no effect on ER association with astral MTs and concentration around spindle poles in early M-phase. In fact, there was a sudden onset of ER association with astral MTs in dynein RNAi cells, revealing activation of an M-phase specific mechanism of ER-MT association. ER redistribution to spindle poles also did not require non-claret disjunctional (ncd), the other known Drosophila MT minus-end motor, nor Klp61F, a MT plus-end motor that generates spindle poleward forces. Collectively, our results suggest that a novel, M-phase specific mechanism of ER-MT association that is independent of MT minus-end motors is required for proper ER partitioning in dividing cells.
Racial and Ethnic Discrimination in the Labor Market for Child Care Teachers

This article examines racial and ethnic discrimination in the child care teacher hiring process. We construct a unique data set that combines a resume audit study of center-based providers with a f...
Mathematics for cryo-electron microscopy

Single-particle cryo-electron microscopy (cryo-EM) has recently joined X-ray crystallography and NMR spectroscopy as a high-resolution structural method for biological macromolecules. Cryo-EM was selected by Nature Methods as Method of the Year 2015, large scale investments in cryo-EM facilities are being made all over the world, and the Nobel Prize in Chemistry 2017 was awarded to Jacques Dubochet, Joachim Frank and Richard Henderson "for developing cryo-electron microscopy for the high-resolution structure determination of biomolecules in solution". This paper focuses on the mathematical principles underlying existing algorithms for structure determination using single particle cryo-EM.
Sharp well-posedness for the generalized KdV of order three on the half line

Abstract In this paper we study the generalized Korteweg–de Vries (KdV) equation with the nonlinear term of order three: ( u 3 + 1 ) x . We prove sharp local well-posedness for the initial and boundary value problem posed on the right half line. We thus close the gap in the well-posedness theory of the generalized KdV which remained open after the seminal work of Colliander and Kenig in Colliander and Kenig (2002).
Absolute pitch can be learned by some adults

Absolute pitch (AP), the rare ability to name any musical note without the aid of a reference note, is thought to depend on an early critical period of development. Although recent research has shown that adults can improve AP performance in a single training session, the best learners still did not achieve note classification levels comparable to performance of a typical, "genuine" AP possessor. Here, we demonstrate that these "genuine" levels of AP performance can be achieved within eight weeks of training for at least some adults, with the best learner passing all measures of AP ability after training and retaining this knowledge for at least four months after training. Alternative explanations of these positive results, such as improving accuracy through adopting a slower, relative pitch strategy, are not supported based on joint analyses of response time and accuracy. The results also did not appear to be driven by extreme familiarity with a single instrument or octave range, as the post-training AP assessments used eight different timbres and spanned over seven octaves. Yet, it is also important to note that a majority of the participants only exhibited modest improvements in performance, suggesting that adult AP learning is difficult and that near-perfect levels of AP may only be achievable by subset of adults. Overall, these results demonstrate that explicit perceptual training in some adults can lead to AP performance that is behaviorally indistinguishable from AP that manifests within a critical period of development. Implications for theories of AP acquisition are discussed.
Repairable Systems Reliability

Nonrepairable systems fail just once, so models for nonrepairable systems must account for random lifetimes. It is often reasonable to assume that different units have random lifetimes that are independent and follow the same distribution, leading to the usual i.i.d. (independent and identically distributed) assumption. By contrast, models for repairable systems must account for successive failures in time. For a given system, these times between failure are often not independent and not identically distributed. Various assumptions about the failure process lead to different models for repairable systems. In this article, we present the nonhomogeneous Poisson process (NHPP), the renewal process, the piecewise exponential model, and the modulated power law process (a compromise between renewal process and NHPP). Inference for a single system as well as for multiple copies of a system is discussed. We also discuss briefly the use of covariates or concomitant variables. Keywords: Poisson process; power law process; renewal process; piecewise exponential; multiple systems
On the Earth Microwave Background: Absorption and Scattering by the Atmosphere

The absorption and scattering of microwave radiation by the atmosphere of the Earth is considered under a steady state scenario. Using this approach, it is demonstrated that the microwave background could not have a cosmological origin. Scientific observations in the microwave region are explained by considering an oceanic source, combined with both Rayleigh and Mie scattering in the atmosphere in the absence of net absorption. Importantly, at high frequencies, Mie scattering occurs primarily with forward propagation. This helps to explain the lack of high frequency microwave background signals when radio antennae are positioned on the Earth’s surface.
Heat Wave Intensity Duration Frequency Curve: A Multivariate Approach for Hazard and Attribution Analysis

Atmospheric warming is projected to intensify heat wave events, as quantified by multiple descriptors, including intensity, duration, and frequency. While most studies investigate one feature at a time, heat wave characteristics are often interdependent and ignoring the relationships between them can lead to substantial biases in frequency (hazard) analyses. We propose a multivariate approach to construct heat wave intensity, duration, frequency (HIDF) curves, which enables the concurrent analysis of all heat wave properties. Here we show how HIDF curves can be used in various locations to quantitatively describe the likelihood of heat waves with different intensities and durations. We then employ HIDF curves to attribute changes in heat waves to anthropogenic warming by comparing GCM simulations with and without anthropogenic emissions. For example, in Los Angeles, CA, HIDF analysis shows that we can attribute the 21% increase in the likelihood of a four-day heat wave (temperature > 31 °C) to anthropogenic emissions.
Transverse centroid oscillations in solenoidially focused beam transport lattices

Abstract Transverse centroid oscillations are analyzed for a beam in a solenoid transport lattice. Linear equations of motion are derived that describe small-amplitude centroid oscillations induced by displacement and rotational misalignments of the focusing solenoids in the transport lattice, dipole steering elements, and initial centroid offset errors. These equations are analyzed in a local rotating Larmor frame to derive complex-variable “alignment functions” and “bending functions” that efficiently describe the characteristics of the centroid oscillations induced by both mechanical misalignments of the solenoids and dipole steering elements. The alignment and bending functions depend only on the properties of the ideal lattice in the absence of errors and steering, and have associated expansion amplitudes set by the misalignments and steering fields, respectively. Applications of this formulation are presented for statistical analysis of centroid oscillations, calculation of actual lattice misalignments from centroid measurements, and optimal beam steering.
Room Temperature Radiation Testing of a 500 °C Durable 4H-SiC JFET Integrated Circuit Technology

Total ionizing dose (TID) and single-event effect (SEE) room-temperature radiation test results are presented for developmental prototype 4H-SiC junction field effect transistor (JFET) semiconductor integrated circuits (ICs) that have demonstrated prolonged operation in extremely high-temperature (500 °C) environments. The devices tested demonstrated over 7 Mrad(Si) TID tolerance and no destructive SEE susceptibility.
Organic Solar Cells Based on Non-fullerene Small-Molecule Acceptors: Impact of Substituent Position

Summary Molecular engineering of non-fullerene small-molecule acceptors (SMAs) plays a key role in enhancing the performance of organic solar cells. An effective strategy is to introduce functional groups into SMA end groups to tune the electronic and morphological properties of polymer/SMA blends. Here, molecular dynamics simulations and long-range corrected density functional theory calculations are combined to examine the impact of the position of methoxy substitution in the SMA end groups. As representative systems, blends of the IT-OM small-molecule acceptor with the PBDB-T polymer donor are explored; three different positions of the methoxy substitution of the IT-OM end groups are examined. By considering intermolecular mixing and packing, the energetic distribution of the charge-transfer electronic states, the exciton-dissociation and non-radiative recombination processes, and the electron-transfer rates among adjacent acceptors, we provide a comprehensive molecular-scale rationalization of the significant experimental variations in device performance for PBDB-T/IT-OM-based solar cells as a function of methoxy position.
A Textual Approach to Harmonizing Sherbert and Smith on Free Exercise Accommodations

The American experiment is permeated by the desire of Americans for freedom of religion. This desire is most famously enshrined in the Free Exercise Clause of the First Amendment of the Constitution, which disables Congress from enacting laws that “prohibit[] the free exercise” of religion.”1 But every freedom must have limits, or it will consume the very government and civilization that seek to safeguard it. Our laws vary in nature from the profound (“thou shalt not kill”) to the seemingly trivial (“yield to pedestrians in crosswalk”), but in many cases, allowing people— any people—not to obey them would be catastrophic. What, then, should be done when the commands of a citizen’s religion conflict with those of the law? How are we to decide when, if ever, the Free Exercise Clause requires an exemption from such a law? In a nutshell, this is the question of religious accommodations—whether and when Americans have the right to engage in religious behavior, even if that behavior violates an otherwise applicable law. On one side of this debate is the extraordinary and essential American value of religious freedom; on the other is the manifest and urgent social need for at least some laws that everyone must obey. This Note chronicles and critiques where twentieth century free exercise jurisprudence has taken us, and offers a suggestion, rooted in the text of the First Amendment and judicial restraint, for where we should go. The Supreme Court began its modern free exercise jurisprudence with a test that, in theory, recognized the importance of protecting religious exercise from government interference, but in practice did little to protect free exercise and much to promote judicial meddling in religious affairs. Part I of this Note examines the compelling interest test of Sherbert v.
Stem-cell-ubiquitous genes spatiotemporally coordinate division through regulation of stem-cell-specific gene networks.

Stem cells are responsible for generating all of the differentiated cells, tissues, and organs in a multicellular organism and, thus, play a crucial role in cell renewal, regeneration, and organization. A number of stem cell type-specific genes have a known role in stem cell maintenance, identity, and/or division. Yet, how genes expressed across different stem cell types, referred to here as stem-cell-ubiquitous genes, contribute to stem cell regulation is less understood. Here, we find that, in the Arabidopsis root, a stem-cell-ubiquitous gene, TESMIN-LIKE CXC2 (TCX2), controls stem cell division by regulating stem cell-type specific networks. Development of a mathematical model of TCX2 expression allows us to show that TCX2 orchestrates the coordinated division of different stem cell types. Our results highlight that genes expressed across different stem cell types ensure cross-communication among cells, allowing them to divide and develop harmonically together. Stem-cell-specific genes regulate processes such as maintenance, identity and/or division. Here, the authors show that in the Arabidopsis root TCX2, a gene expressed across different stem cell populations (a stem-cell-ubiquitous gene), controls division and identity by regulating stem-cell-type-specific networks.
Soft X-ray observations of pre-main sequence stars in the chamaeleon dark cloud

Einstein IPC observations of the nearby Chamaeleon I star forming cloud show 22 well-resolved soft X-ray sources in a 1x2 deg region. Twelve are associated with H-alpha emission line pre-main sequence (PMS) stars, and four with optically selected PMS stars. Several X-ray sources have two or more PMS stars in their error circles. Optical spectra were obtained at CTIO of possible stellar counterparts of the remaining X-ray sources. They reveal 5 probable new cloud members, K7-MO stars with weak or absent emission lines. These naked X-ray selected PMS stars are similar to those found in the Taurus-Auriga cloud. The spatial distributions and H-R diagrams of the X-ray and optically selected PMS stars in the cloud are very similar. Luminosity functions indicate the Chamaeleon stars are on average approximately 5 times more X-ray luminous than Pleiad dwarfs. A significant correlation between L sub x and optical magnitude suggests this trend may continue within the PMS phase of stellar evolution. The relation of increasing X-ray luminosity with decreasing stellar ages is thus extended to stellar ages as young as 1 million years.
Efficiently Counting Vertex Orbits of All 5-vertex Subgraphs, by EVOKE

Subgraph counting is a fundamental task in network analysis. Typically, algorithmic work is on total counting, where we wish to count the total frequency of a (small) pattern subgraph in a large input data set. But many applications require local counts (also called vertex orbit counts) wherein, for every vertex v of the input graph, one needs the count of the pattern subgraph involving v. This provides a rich set of vertex features that can be used in machine learning tasks, especially classification and clustering. But getting local counts is extremely challenging. Even the easier problem of getting total counts has received much research attention. Local counts require algorithms that get much finer grained information, and the sheer output size makes it difficult to design scalable algorithms. We present EVOKE, a scalable algorithm that can determine vertex orbits counts for all 5-vertex pattern subgraphs. In other words, EVOKE exactly determines, for every vertex v of the input graph and every 5-vertex subgraph H, the number of copies of H that v participates in. EVOKE can process graphs with tens of millions of edges, within an hour on a commodity machine. EVOKE is typically hundreds of times faster than previous state of the art algorithms, and gets results on datasets beyond the reach of previous methods. Theoretically, we generalize a recent "graph cutting" framework to get vertex orbit counts. This framework generate a collection of polynomial equations relating vertex orbit counts of larger subgraphs to those of smaller subgraphs. EVOKE carefully exploits the structure among these equations to rapidly count. We prove and empirically validate that EVOKE only has a small constant factor overhead over the best (total) 5-vertex subgraph counter.
Quantum nonlinear mixing of thermal photons to surpass the blackbody limit

Nearly all thermal radiation phenomena involving materials with linear response can be accurately described via semi-classical theories of light. Here, we go beyond these traditional paradigms to study a nonlinear system that, as we show, requires quantum theory of damping. Specifically, we analyze thermal radiation from a resonant system containing a χ(2) nonlinear medium and supporting resonances at frequencies ω1 and ω2 ≈ 2ω1, where both resonators are driven only by intrinsic thermal fluctuations. Within our quantum formalism, we reveal new possibilities for shaping the thermal radiation. We show that the resonantly enhanced nonlinear interaction allows frequency-selective enhancement of thermal emission through upconversion, surpassing the well-known blackbody limits associated with linear media. Surprisingly, we also find that the emitted thermal light exhibits non-trivial statistics (g(2)(0) ≠ ~2) and biphoton intensity correlations (at two distinct frequencies). We highlight that these features can be observed in the near future by heating a properly designed nonlinear system, without the need for any external signal. Our work motivates new interdisciplinary inquiries combining the fields of nonlinear photonics, quantum optics and thermal science.
GANPOP: Generative Adversarial Network Prediction of Optical Properties From Single Snapshot Wide-Field Images

We present a deep learning framework for wide-field, content-aware estimation of absorption and scattering coefficients of tissues, called Generative Adversarial Network Prediction of Optical Properties (GANPOP). Spatial frequency domain imaging is used to obtain ground-truth optical properties at 660 nm from in vivo human hands and feet, freshly resected human esophagectomy samples, and homogeneous tissue phantoms. Images of objects with either flat-field or structured illumination are paired with registered optical property maps and are used to train conditional generative adversarial networks that estimate optical properties from a single input image. We benchmark this approach by comparing GANPOP to a single-snapshot optical property (SSOP) technique, using a normalized mean absolute error (NMAE) metric. In human gastrointestinal specimens, GANPOP with a single structured-light input image estimates the reduced scattering and absorption coefficients with 60% higher accuracy than SSOP while GANPOP with a single flat-field illumination image achieves similar accuracy to SSOP. When applied to both in vivo and ex vivo swine tissues, a GANPOP model trained solely on structured-illumination images of human specimens and phantoms estimates optical properties with approximately 46% improvement over SSOP, indicating adaptability to new, unseen tissue types. Given a training set that appropriately spans the target domain, GANPOP has the potential to enable rapid and accurate wide-field measurements of optical properties.
Product Lineups: The More You Search, The Less You Find

Consumers often try to visually identify a previously encountered product among a sequence of similar items, guided only by their memory and a few general search terms. What determines their success at correctly identifying the target product in such “product lineups”? The current research finds that the longer consumers search sequentially, the more conservative and—ironically—inaccurate judges they become. Consequently, the more consumers search, the more likely they are to erroneously reject the correct target when it finally appears in the lineup. This happens because each time consumers evaluate a similar item in the lineup, and determine that it is not the option for which they have been looking, they draw an implicit inference that the correct target should feel more familiar than the similar items rejected up to that point. This causes the subjective feeling of familiarity consumers expect to experience with the true target to progressively escalate, making them more conservative but also less accurate judges. The findings have practical implications for consumers and marketers, and make theoretical contributions to research on inference-making, online search, and product recognition.
Political institutions, resources, and war: Theory and evidence from ancient Rome

Abstract How does a governing coalition’s size affect the extent and type of violence in society? The model developed here predicts that larger coalitions are less likely to fight for private goods (e.g., plunder) than for public goods (e.g., defense), yet this substitution need not reduce the overall scale of fighting. That prediction is tested by investigating how Rome’s transition from Republic to Empire affected military patterns. The raw data and three empirical tests suggest that the Republic engaged in more battles overall and that Republican battles had more of a public goods component. This study furthers our empirical knowledge about the ancient world while bringing data to bear on contemporary debates about the causes of peace and war.
Faster update time for turnstile streaming algorithms

In this paper, we present a new algorithm for maintaining linear sketches in turnstile streams with faster update time. As an application, we show that log n Count sketches or CountMin sketches with a constant number of columns (i.e., buckets) can be implicitly maintained in worst-case O(log0.582 n) update time using O(log n) words of space, on a standard word RAM with word-size w = Θ(log n). The exponent 0.582 a 2ω/3 − 1, where ω is the current matrix multiplication exponent. Due to the numerous applications of linear sketches, our algorithm improves the update time for many streaming problems in turnstile streams, in the high success probability setting, without using more space, including e2 norm estimation, e2 heavy hitters, point query with e1 or e2 error, etc. Our algorithm generalizes, with the same update time and space, to maintaining log n linear sketches, where each sketch 1. partitions the coordinates into k o(1) n buckets using a c-wise independent hash function for constant c, 2. maintains the sum of coordinates for each bucket. Moreover, if arbitrary word operations are allowed, the update time can be further improved to O(log0.187 n), where 0.187 a ω/2 − 1. Our update algorithm is adaptive, and it circumvents the non-adaptive cell-probe lower bounds for turnstile streaming algorithms by Larsen, Nelson and Nguyen (STOC'15). On the other hand, our result also shows that proving unconditional cell-probe lower bound for the update time seems very difficult, even if the space is restricted to be (nearly) the optimum. If ω = 2, the cell-probe update time of our algorithm would be logo(1) n. Hence, proving any higher lower bound would imply ω > 2.
Optogenetic modulation of cortical neurons using organic light emitting diodes (OLEDs).

Objective There is a need for low power, scalable photoelectronic devices and systems for emerging optogenetic needs in neuromodulation. Conventional light emitting diodes (LEDs) are constrained by power and lead-counts necessary for scalability. Organic LEDs (OLEDs) offer an exciting approach to decrease power and lead-counts while achieving high channel counts on thin, flexible substrates that conform to brain surfaces or peripheral neuronal fibers. In this study, we investigate the potential for using OLEDs to modulate neuronal networks cultured in vitro on a transparent microelectrode array (MEA) and subsequently validate neurostimulation in vivo in a transgenic mouse model. Approach Cultured mouse cortical neurons were transfected with light-sensitive opsins such as blue-light sensitive channel-rhodopsin (ChR2) and green-light sensitive chimeric channel-rhodopsin (C1V1tt) and stimulated using blue and green OLEDs (with 455 and 520 nm peak emission spectra respectively) at a power of ~1 mW mm-2 under pulsed conditions. Main results We demonstrate neuromodulation and optostimulus-locked, single unit-neuronal activity in neurons expressing stimulating opsins (34 units on n = 4 MEAs, each with 16 recordable channels). We also validated the optostimulus-locked response in preliminary experiments in a channel-rhodopsin expressing transgenic mouse model, where at least three isolatable single neuronal cortical units respond to OLED stimulation. Significance The above results indicate the feasibility of generating sufficient luminance from OLEDs to perform neuromodulation both in vitro and in vivo. This opens up the possibility of developing thin, flexible OLED films with multiple stimulation sites that can conform to the shape of the neuronal targets in the brain or the peripheral nervous system. However, stability of these OLEDs under chronic conditions still needs to be carefully assessed with appropriate packaging approaches.
Deep Neural Network Ensembles

Current deep neural networks suffer from two problems; first, they are hard to interpret, and second, they suffer from overfitting. There have been many attempts to define interpretability in neural networks, but they typically lack causality or generality. A myriad of regularization techniques have been developed to prevent overfitting, and this has driven deep learning to become the hot topic it is today; however, while most regularization techniques are justified empirically and even intuitively, there is not much underlying theory. This paper argues that to extract the features used in neural networks to make decisions, it’s important to look at the paths between clusters existing in the hidden spaces of neural networks. These features are of particular interest because they reflect the true decision-making process of the neural network. This analysis is then furthered to present an ensemble algorithm for arbitrary neural networks which has guarantees for test accuracy. Finally, a discussion detailing the aforementioned guarantees is introduced and the implications to neural networks, including an intuitive explanation for all current regularization methods, are presented. The ensemble algorithm has generated state-of-the-art results for Wide-ResNets on CIFAR-10 (top 5 for all models) and has improved test accuracy for all models it has been applied to.
An overview of distance and similarity functions for structured data

The notions of distance and similarity play a key role in many machine learning approaches, and artificial intelligence in general, since they can serve as an organizing principle by which individuals classify objects, form concepts and make generalizations. While distance functions for propositional representations have been thoroughly studied, work on distance functions for structured representations, such as graphs, frames or logical clauses, has been carried out in different communities and is much less understood. Specifically, a significant amount of work that requires the use of a distance or similarity function for structured representations of data usually employs ad-hoc functions for specific applications. Therefore, the goal of this paper is to provide an overview of this work to identify connections between the work carried out in different areas and point out directions for future work.
Probing RNA Conformational Equilibria within the Functional Cellular Context.

Summary Low-abundance short-lived non-native conformations referred to as excited states (ESs) are increasingly observed in vitro and implicated in the folding and biological activities of regulatory RNAs. We developed an approach for assessing the relative abundance of RNA ESs within the functional cellular context. Nuclear magnetic resonance (NMR) spectroscopy was used to estimate the degree to which substitution mutations bias conformational equilibria toward the inactive ES in vitro. The cellular activity of the ES-stabilizing mutants was used as an indirect measure of the conformational equilibria within the functional cellular context. Compensatory mutations that restore the ground-state conformation were used to control for changes in sequence. Using this approach, we show that the ESs of two regulatory RNAs from HIV-1, the transactivation response element (TAR) and the Rev response element (RRE), likely form in cells with abundances comparable to those measured in vitro, and their targeted stabilization may provide an avenue for developing anti-HIV therapeutics.
TreeSwift: A massively scalable Python tree package

Abstract Phylogenetic trees are essential to evolutionary biology, and numerous methods exist that attempt to extract phylogenetic information applicable to a wide range of disciplines, such as epidemiology and metagenomics. Currently, the three main Python packages for trees are Bio.Phylo, DendroPy, and the ETE Toolkit, but as dataset sizes grow, parsing and manipulating ultra-large trees becomes impractical for these tools. To address this issue, we present TreeSwift, a user-friendly and massively scalable Python package for traversing and manipulating trees that is ideal for algorithms performed on ultra-large trees.
An Individual Interneuron Participates in Many Kinds of Inhibition and Innervates Much of the Mouse Visual Thalamus

One way to assess a neuron's function is to describe all its inputs and outputs. With this goal in mind, we used serial section electron microscopy to map 899 synaptic inputs and 623 outputs in one inhibitory interneuron in a large volume of the mouse visual thalamus. This neuron innervated 256 thalamocortical cells spread across functionally distinct subregions of the visual thalamus. All but one of its neurites were bifunctional, innervating thalamocortical and local interneurons while also receiving synapses from the retina. We observed a wide variety of local synaptic motifs. While this neuron innervated many cells weakly, with single en passant synapses, it also deployed specialized branches that climbed along other dendrites to form strong multi-synaptic connections with a subset of partners. This neuron's diverse range of synaptic relationships allows it to participate in a mix of global and local processing but defies assigning it a single circuit function.
An isoperimetric inequality for the Hamming cube and some consequences

Our basic result, an isoperimetric inequality for Hamming cube $Q_n$, can be written: \[ \int h_A^\beta d\mu \ge 2 \mu(A)(1-\mu(A)). \] Here $\mu$ is uniform measure on $V=\{0,1\}^n$ ($=V(Q_n)$); $\beta=\log_2(3/2)$; and, for $S\subseteq V$ and $x\in V$, \[ h_S(x) = \begin{cases} d_{V \setminus S}(x) &\mbox{ if } x \in S, \\ 0 &\mbox{ if } x \notin S \end{cases} \] (where $d_T(x)$ is the number of neighbors of $x$ in $T$). This implies inequalities involving mixtures of edge and vertex boundaries, with related stability results, and suggests some more general possibilities. One application, a stability result for the set of edges connecting two disjoint subsets of $V$ of size roughly $|V|/2$, is a key step in showing that the number of maximal independent sets in $Q_n$ is $(1+o(1))2n\exp_2[2^{n-2}]$. This asymptotic statement, whose proof will appear separately, was the original motivation for the present work.
On Distributed Stochastic Gradient Algorithms for Global Optimization

The paper considers the problem of network-based computation of global minima in smooth nonconvex optimization problems. It is known that distributed gradient-descent-type algorithms can achieve convergence to the set of global minima by adding slowly decaying Gaussian noise in order to escape local minima. However, the technical assumptions under which convergence is known to occur can be restrictive in practice. In particular, in known convergence results, the local objective functions possessed by agents are required to satisfy a highly restrictive bounded-gradient-dissimilarity condition. The paper demonstrates convergence to the set of global minima while relaxing this key assumption.
Cytokinesis and postabscission midbody remnants are regulated during mammalian brain development

Building a brain of the proper size and structure requires neural stem cells (NSCs) to divide with tight temporal and spatial control to produce different daughter cell types in proper numbers and sequence. Mammalian NSCs in the embryonic cortex must maintain their polarized epithelial structure as they undergo both early proliferative divisions and later neurogenic divisions. To do this, they undergo a polarized form of cytokinesis at the apical membrane that is not well understood. Here, we investigate whether polarized furrowing and abscission in mouse NSCs are regulated differently at earlier and later stages and in a cytokinesis mutant, Kif20b This mutant was previously shown to have microcephaly and elevated apoptosis of NSCs. We developed methods to live image furrow ingression and midbody abscission in NSCs within cortical explants. We find that polarized furrow ingression occurs at a steady rate and completes in ∼15 min at two different ages. However, ingression is slower in a subset of Kif20b mutant NSCs. Abscission is usually observed on both sides of the midbody and takes 65 to 75 min to complete. Surprisingly, abscission is accelerated in the Kif20b mutant NSCs. Postabscission midbody remnants are observed at the apical membranes of daughter cells and are much more abundant in early-stage cortices. After NSC divisions in vitro, midbody remnants are more often retained on the daughter cells of early proliferative divisions. Altogether, these results suggest that regulation of abscission timing and midbody remnants in embryonic NSCs may influence proper brain growth and structure.
Speech-Based Parameter Estimation of an Asymmetric Vocal Fold Oscillation Model and its Application in Discriminating Vocal Fold Pathologies

So far, several physical models have been proposed for the study of vocal fold oscillations during phonation. The parameters of these models, such as vocal fold elasticity, resistance, etc. are traditionally determined through the observation and measurement of the vocal fold vibrations in the larynx. Since such direct measurements tend to be the most accurate, the traditional practice has been to set the parameter values of these models based on measurements that are averaged across an ensemble of human subjects. However, the direct measurement process is hard to revise outside of clinical settings. In many cases, especially in pathological ones, the properties of the vocal folds often deviate from their generic values—sometimes asymmetrically wherein the characteristics of the two vocal folds differ for the same individual. In such cases, it is desirable to find a more scalable way to adjust the model parameters on a case by case basis. In this paper, we present a novel and alternate way to determine vocal fold model parameters from the speech signal. We focus on an asymmetric model and show that for such models, differences in estimated parameters can be successfully used to discriminate between voices that are characteristic of different underlying vocal fold pathologies.
E‐Cigarette Use Increases Susceptibility to Bacterial Infection by Impairment of Human Neutrophil Chemotaxis, Phagocytosis and NET Formation

E-cigarettes are portrayed as safer relative to conventional tobacco. However, burgeoning evidence suggests that E-cigarettes may adversely affect host defenses. However, the precise mechanisms by ...
Practitioner Perspectives on Hypothesis Testing Strategies in the Context of Functional Behavior Assessment.

When results of descriptive functional behavior assessments (FBAs) are inconclusive, experimental analysis methods can be used to directly test hypotheses about when or why a student engages in challenging behavior. Despite growing research on practical variations of hypothesis testing in schools, these methods are rarely incorporated in FBAs in practice. To inform practitioner perspectives on hypothesis testing as a component of FBAs, we surveyed a statewide sample of school practitioners who participate in the FBA process (n = 253). The survey addressed their views on three hypothesis testing strategies (i.e., functional analysis, antecedent analysis, concurrent operant analysis) across several dimensions. On 5-point Likert type scales, participants rated acceptability of procedures, feasibility of implementation, utility of results, conditions in which they would likely use or recommend the strategy, and perceived barriers. We used descriptive and nonparametric statistical analyses to compare ratings among the three hypothesis testing strategies. The majority of participants rated all three strategies favorably with respect to utility. However, ratings on acceptability, feasibility, and barriers were differentiated and favored the concurrent operant analysis over the functional and antecedent analyses. We discuss implications for practice and future research on incorporating hypothesis testing in FBAs for students with persistent challenging behavior.
Seniority Orders between Inside Debts and External Debts

In this paper, we study the seniority orders between a firm's external debts and its inside-debt compensation to its manager and analyze how different seniority orders influence the equilibrium ins...
Variation in performance of commonly used statistical methods for estimating effectiveness of state-level opioid policies on opioid-related mortality

Over the last two decades, there has been a surge of opioid-related overdose deaths resulting in a myriad of state policy responses. Researchers have evaluated the effectiveness of such policies using a wide-range of statistical models, each of which requires multiple design choices that can influence the accuracy and precision of the estimated policy effects. This simulation study used real-world data to compare model performance across a range of important statistical constructs to better understand which methods are appropriate for measuring the impacts of state-level opioid policies on opioid-related mortality. Our findings show that many commonly-used methods have very low statistical power to detect a significant policy effect (
Investigation of the Optical Properties of Diamond-Like Carbon Films

Hydrogenated-amorphous carbon films are deposited in a microwave ECR plasma reactor with an rf biased substrate holder using methane-argon and acetylene-argon gas mixtures. The film’s optical properties are characterized versus deposition conditions including pressure (1–5 mTorr), rf induced bias (-100 to 20 volts), and argon/hydrocarbon flow ratio. The acetylene-argon discharges show that ion species formed from acetylene are more important than the argon ions, conversely, the methane-argon discharges showed that argon ions are a major ionic species in the deposition. The influence of the thickness of insulating substrates is studied and the feasibility of depositing multilayer a-C:H films with different film properties in each layer is demonstrated.
A Parallel Algorithm For Anonymizing Large-scale Trajectory Data

With the proliferation of location-based services enabled by a large number of mobile devices and applications, the quantity of location data, such as trajectories collected by service providers, is gigantic. If these datasets could be published, then they would be valuable assets to various service providers to explore business opportunities, to study commuter behavior for better transport management, which in turn benefits the general public for day-to-day commuting. However, there are two major concerns that considerably limit the availability and the usage of these trajectory datasets. The first is the threat to individual privacy, as users’ trajectories may be misused to discover sensitive information, such as home locations, their children’s school locations, or social information like habits or relationships. The other concern is the ability to analyze the exabytes of location data in a timely manner. Although there have been trajectory anonymization approaches proposed in the past to mitigate privacy concerns. None of these prior works address the scalability issue, since it is a newly occurring problem brought by the significantly increasing adoption of location-based services. In this article, we conquer these two challenges by designing a novel parallel trajectory anonymization algorithm that achieves scalability, strong privacy protection, and high utility rate of the anonymized trajectory datasets. We have conducted extensive experiments using MapReduce and Spark on real maps with different topologies, and our results prove both effectiveness and efficiency when compared with the centralized approaches.
Stopping High Inflation; An Analytical Overview

This paper reviews the evidence on stabilization plans in high inflation countries within a unified theoretical framework. The evidence suggests that hyperinflations have been stopped almost instantaneously with no major output costs, while stabilization programs in chronic inflation countries have resulted in an initial expansion followed by a later recession, in addition to a sustained real exchange rate appreciation and current account deficits. These outcomes turn out to be consistent with the predictions of the analytical model.
THE EFFECT OF EXPECTED INCOME ON INDIVIDUAL MIGRATION DECISIONS

This paper develops a tractable econometric model of optimal migration, focusing on expected income as the main economic influence on migration. The model improves on previous work in two respects: it covers optimal sequences of location decisions (rather than a single once-for-all choice) and it allows for many alternative location choices. The model is estimated using panel data from the National Longitudinal Survey of Youth on white males with a high-school education. Our main conclusion is that interstate migration decisions are influenced to a substantial extent by income prospects. The results suggest that the link between income and migration decisions is driven both by geographic differences in mean wages and by a tendency to move in search of a better locational match when the income realization in the current location is unfavorable.
Two-row W-graphs in affine type A

Abstract For affine symmetric groups we construct finite W-graphs corresponding to two-row shapes, and prove their uniqueness. This gives the first non-trivial family of purely combinatorial constructions of finite W-graphs in an affine type. We compare our construction with quotients of periodic W-graphs defined by Lusztig. Under certain positivity assumption on the latter the two are shown to be isomorphic.
Real-Time Data Revisions and the PCE Measure of Inflation

This paper tracks data revisions in the Personal Consumption Expenditure using the exclusions-from-core inflation persistence model. Keeping the number of observations the same, the regression parameters of earlier vintages of real-time data, beginning with vintage 1996:Q1, are tested for coincidence against the regression parameters of the last vintage of real-time data, used in this paper, which is vintage 2008:Q2 in a parametric and two nonparametric frameworks. The effects of data revisions are not detectable in the vast majority of cases in the parametric model, but the flexibility of the two nonparametric models is able to utilize the data revisions.
The ideological divide in public perceptions of self-driving cars.

Applications in artificial intelligence such as self-driving cars may profoundly transform our society, yet emerging technologies are frequently faced with suspicion or even hostility. Meanwhile, p...
RelNet: End-to-end Modeling of Entities & Relations.

We introduce RelNet: a new model for relational reasoning. RelNet is a memory augmented neural network which models entities as abstract memory slots and is equipped with an additional relational memory which models relations between all memory pairs. The model thus builds an abstract knowledge graph on the entities and relations present in a document which can then be used to answer questions about the document. It is trained end-to-end: only supervision to the model is in the form of correct answers to the questions. We test the model on the 20 bAbI question-answering tasks with 10k examples per task and find that it solves all the tasks with a mean error of 0.3%, achieving 0% error on 11 of the 20 tasks.