cssi/SciSciGPT-SciSciNet huggingface dataset:
Comparison of single and module-based methods for modeling gene regulatory networks.

MOTIVATION Gene regulatory networks describe the regulatory relationships among genes, and developing methods for reverse engineering these networks is an ongoing challenge in computational biology. The majority of the initially proposed methods for gene regulatory network discovery create a network of genes and then mine it in order to uncover previously unknown regulatory processes. More recent approaches have focused on inferring modules of co-regulated genes, linking these modules with regulatory genes and then mining them to discover new molecular biology. RESULTS In this work we analyze module-based network approaches to build gene regulatory networks, and compare their performance to single gene network approaches. In the process, we propose a novel approach to estimate gene regulatory networks drawing from the module-based methods. We show that generating modules of co-expressed genes which are predicted by a sparse set of regulators using a variational Bayes method, and then building a bipartite graph on the generated modules using sparse regression, yields more informative networks than previous single and module-based network approaches as measured by: (i) the rate of enriched gene sets, (ii) a network topology assessment, (iii) ChIP-Seq evidence and (iv) the KnowEnG Knowledge Network collection of previously characterized gene-gene interactions. AVAILABILITY AND IMPLEMENTATION The code is written in R and can be downloaded from https://github.com/mikelhernaez/linker. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Parameter-Free Online Convex Optimization with Sub-Exponential Noise

We consider the problem of unconstrained online convex optimization (OCO) with sub-exponential noise, a strictly more general problem than the standard OCO. In this setting, the learner receives a subgradient of the loss functions corrupted by sub-exponential noise and strives to achieve optimal regret guarantee, without knowledge of the competitor norm, i.e., in a parameter-free way. Recently, Cutkosky and Boahen (COLT 2017) proved that, given unbounded subgradients, it is impossible to guarantee a sublinear regret due to an exponential penalty. This paper shows that it is possible to go around the lower bound by allowing the observed subgradients to be unbounded via stochastic noise. However, the presence of unbounded noise in unconstrained OCO is challenging; existing algorithms do not provide near-optimal regret bounds or fail to have a guarantee. So, we design a novel parameter-free OCO algorithm for Banach space, which we call BANCO, via a reduction to betting on noisy coins. We show that BANCO achieves the optimal regret rate in our problem. Finally, we show the application of our results to obtain a parameter-free locally private stochastic subgradient descent algorithm, and the connection to the law of iterated logarithms.
Comparison of nonlinear microscopy and frozen section histology for imaging of Mohs surgical margins

Mohs surgery uses en face frozen section analysis (FSA) with complete margin examination for the excision of select basal cell carcinomas (BCC), obtaining excellent cosmetic outcomes and extremely low recurrence rates. However, Mohs with FSA is time-consuming because of the need to iteratively perform cryosectioning on sequential excisions. Fluorescent microscopies can image tissue specimens without requiring physical sectioning, potentially reducing the time to perform Mohs surgery. We demonstrate a protocol for nonlinear microscopy (NLM) imaging of surgical specimens that combines dual agent staining, virtual H&E rendering, and video rate imaging. We also introduce a novel protocol that enables micron-level co-registration of NLM images with FSA histology, and demonstrate that NLM can reproduce similar features similar to FSA in BCC specimens with both negative and positive surgical margins. We show that the fluorescent labels can be extracted with conventional vacuum infiltration processing, enabling subsequent immunohistochemistry on fluorescently labeled tissue. This protocol can also be applied to evaluate the performance of NLM compared with FSA in a wide range of pathologies for intraoperative consultation.
Type-2-Diabetes Alters CSF but Not Plasma Metabolomic and AD Risk Profiles in Vervet Monkeys.

Epidemiological studies suggest that individuals with type 2 diabetes (T2D) have a twofold to fourfold increased risk for developing Alzheimer's disease (AD), however, the exact mechanisms linking the two diseases are unknown. In both conditions, the majority of pathophysiological changes, including glucose and insulin dysregulation, insulin resistance, and AD-related changes in Aβ and tau, occur decades before the onset of clinical symptoms and diagnosis. In this study, we investigated the relationship between metabolic biomarkers associated with T2D and amyloid pathology including Aβ levels, from cerebrospinal fluid (CSF) and fasting plasma of healthy, pre-diabetic (PreD), and T2D vervet monkeys (Chlorocebus aethiops sabaeus). Consistent with the human disease, T2D monkeys have increased plasma and CSF glucose levels as they transition from normoglycemia to PreD and diabetic states. Although plasma levels of acylcarnitines and amino acids remained largely unchanged, peripheral hyperglycemia correlated with decreased CSF acylcarnitines and CSF amino acids, including branched chain amino acid (BCAA) concentrations, suggesting profound changes in cerebral metabolism coincident with systemic glucose dysregulation. Moreover, CSF Aβ 40 and CSF Aβ 42 levels decreased in T2D monkeys, a phenomenon observed in the human course of AD which coincides with increased amyloid deposition within the brain. In agreement with previous studies in mice, CSF Aβ 40 and CSF Aβ 42 were highly correlated with CSF glucose levels, suggesting that glucose levels in the brain are associated with changes in Aβ metabolism. Interestingly, CSF Aβ 40 and CSF Aβ 42 levels were also highly correlated with plasma but not CSF lactate levels, suggesting that plasma lactate might serve as a potential biomarker of disease progression in AD. Moreover, CSF glucose and plasma lactate levels were correlated with CSF amino acid and acylcarnitine levels, demonstrating alterations in cerebral metabolism occurring with the onset of T2D. Together, these data suggest that peripheral metabolic changes associated with the development of T2D produce alterations in brain metabolism that lead to early changes in the amyloid cascade, similar to those observed in pre-symptomatic AD.
Meiotic cellular rejuvenation is coupled to nuclear remodeling in budding yeast

Production of healthy gametes in meiosis relies on the quality control and proper distribution of both nuclear and cytoplasmic contents. Meiotic differentiation naturally eliminates age-induced cellular damage by an unknown mechanism. Using time-lapse fluorescence microscopy in budding yeast, we found that nuclear senescence factors - including protein aggregates, extrachromosomal ribosomal DNA circles, and abnormal nucleolar material - are sequestered away from chromosomes during meiosis II and subsequently eliminated. A similar sequestration and elimination process occurs for the core subunits of the nuclear pore complex in both young and aged cells. Nuclear envelope remodeling drives the formation of a membranous compartment containing the sequestered material. Importantly, de novo generation of plasma membrane is required for the sequestration event, preventing the inheritance of long-lived nucleoporins and senescence factors into the newly formed gametes. Our study uncovers a new mechanism of nuclear quality control and provides insight into its function in meiotic cellular rejuvenation.
RaD-VIO: Rangefinder-aided Downward Visual-Inertial Odometry

State-of-the-art forward facing monocular visual-inertial odometry algorithms are often brittle in practice, especially whilst dealing with initialisation and motion in directions that render the state unobservable. In such cases having a reliable complementary odometry algorithm enables robust and resilient flight. Using the common local planarity assumption, we present a fast, dense, and direct frame-to-frame visual-inertial odometry algorithm for downward facing cameras that minimises a joint cost function involving a homography based photometric cost and an IMU regularisation term. Via extensive evaluation in a variety of scenarios we demonstrate superior performance than existing state-of-the-art downward facing odometry algorithms for Micro Aerial Vehicles (MAVs).
Stress-induced phospho-ubiquitin formation causes parkin degradation

Mutations in the E3 ubiquitin ligase parkin are the most common known cause of autosomal recessive Parkinson’s disease (PD), and parkin depletion may play a role in sporadic PD. Here, we sought to elucidate the mechanisms by which stress decreases parkin protein levels using cultured neuronal cells and the PD-relevant stressor, L-DOPA. We find that L-DOPA causes parkin loss through both oxidative stress-independent and oxidative stress-dependent pathways. Characterization of the latter reveals that it requires both the kinase PINK1 and parkin’s interaction with phosphorylated ubiquitin (phospho-Ub) and is mediated by proteasomal degradation. Surprisingly, autoubiquitination and mitophagy do not appear to be required for such loss. In response to stress induced by hydrogen peroxide or CCCP, parkin degradation also requires its association with phospho-Ub, indicating that this mechanism is broadly generalizable. As oxidative stress, metabolic dysfunction and phospho-Ub levels are all elevated in PD, we suggest that these changes may contribute to a loss of parkin expression.
Finite Element Convergence for State-Based Peridynamic Fracture Models

We establish the a priori convergence rate for finite element approximations of a class of nonlocal nonlinear fracture models. We consider state-based peridynamic models where the force at a material point is due to both the strain between two points and the change in volume inside the domain of the nonlocal interaction. The pairwise interactions between points are mediated by a bond potential of multi-well type while multi-point interactions are associated with the volume change mediated by a hydrostatic strain potential. The hydrostatic potential can either be a quadratic function, delivering a linear force–strain relation, or a multi-well type that can be associated with the material degradation and cavitation. We first show the well-posedness of the peridynamic formulation and that peridynamic evolutions exist in the Sobolev space $$H^2$$. We show that the finite element approximations converge to the $$H^2$$ solutions uniformly as measured in the mean square norm. For linear continuous finite elements, the convergence rate is shown to be $$C_t \Delta t + C_s h^2/\epsilon ^2$$, where $$\epsilon $$ is the size of the horizon, h is the mesh size, and $$\Delta t$$ is the size of the time step. The constants $$C_t$$ and $$C_s$$ are independent of $$\Delta t$$ and h and may depend on $$\epsilon $$ through the norm of the exact solution. We demonstrate the stability of the semi-discrete approximation. The stability of the fully discrete approximation is shown for the linearized peridynamic force. We present numerical simulations with the dynamic crack propagation that support the theoretical convergence rate.
Sentence-Level Content Planning and Style Specification for Neural Text Generation.

Building effective text generation systems requires three critical components: content selection, text planning, and surface realization, and traditionally they are tackled as separate problems. Recent all-in-one style neural generation models have made impressive progress, yet they often produce outputs that are incoherent and unfaithful to the input. To address these issues, we present an end-to-end trained two-step generation model, where a sentence-level content planner first decides on the keyphrases to cover as well as a desired language style, followed by a surface realization decoder that generates relevant and coherent text. For experiments, we consider three tasks from domains with diverse topics and varying language styles: persuasive argument construction from Reddit, paragraph generation for normal and simple versions of Wikipedia, and abstract generation for scientific articles. Automatic evaluation shows that our system can significantly outperform competitive comparisons. Human judges further rate our system generated text as more fluent and correct, compared to the generations by its variants that do not consider language style.
Numerical simulation of growth of a vapor bubble during flow boiling of water in a microchannel

The present study is performed to numerically analyze the growth of a vapor bubble during flow of water through a microchannel. The complete Navier–Stokes equations, along with continuity and energy equations, are solved using the SIMPLER (semi-implicit method for pressure-linked equations revised) method. The liquid–vapor interface is captured using the level set technique. The microchannel is 200-μm square in cross-section and the bubble is placed at the center of the channel with superheated liquid around it. The results show steady initial bubble growth followed by a rapid axial expansion after the bubble fills the channel cross-section. A trapped liquid layer is observed between the bubble and the channel as it elongates. The bubble growth rate increased with the incoming liquid superheat, but decreased with Reynolds number. The formation of a vapor patch at the walls is found to be dependent on the time the bubble takes to fill up the channel. The upstream interface of the bubble is found to exhibit both forward and reverse movement during bubble growth. The results show little effect of gravity on the bubble growth rate under the specified conditions. The bubble growth features obtained from the numerical results are found to be qualitatively similar to experimental observations.
Investigating Bert's knowledge of language: Five analysis methods with NPIs

Though state-of-the-art sentence representation models can perform tasks requiring significant knowledge of grammar, it is an open question how best to evaluate their grammatical knowledge. We explore five experimental methods inspired by prior work evaluating pretrained sentence representation models. We use a single linguistic phenomenon, negative polarity item (NPI) licensing, as a case study for our experiments. NPIs like any are grammatical only if they appear in a licensing environment like negation (Sue doesn’t have any cats vs. *Sue has any cats). This phenomenon is challenging because of the variety of NPI licensing environments that exist. We introduce an artificially generated dataset that manipulates key features of NPI licensing for the experiments. We find that BERT has significant knowledge of these features, but its success varies widely across different experimental methods. We conclude that a variety of methods is necessary to reveal all relevant aspects of a model’s grammatical knowledge in a given domain.
Iterative Least Trimmed Squares for Mixed Linear Regression

Given a linear regression setting, Iterative Least Trimmed Squares (ILTS) involves alternating between (a) selecting the subset of samples with lowest current loss, and (b) re-fitting the linear model only on that subset. Both steps are very fast and simple. In this paper, we analyze ILTS in the setting of mixed linear regression with corruptions (MLR-C). We first establish deterministic conditions (on the features etc.) under which the ILTS iterate converges linearly to the closest mixture component. We also provide a global algorithm that uses ILTS as a subroutine, to fully solve mixed linear regressions with corruptions. We then evaluate it for the widely studied setting of isotropic Gaussian features, and establish that we match or better existing results in terms of sample complexity. Finally, we provide an ODE analysis for a gradient-descent variant of ILTS that has optimal time complexity. Our results provide initial theoretical evidence that iteratively fitting to the best subset of samples -- a potentially widely applicable idea -- can provably provide state of the art performance in bad training data settings.
Industry relatedness, FDI liberalization and the indigenous innovation process in China

ABSTRACTThis paper employs a structural innovation model to study the process of indigenous innovation in China and the role of industry relatedness. To better take into account China's transitioni...
Identification of peptide coatings that enhance diffusive transport of nanoparticles through the tumor microenvironment

In solid tumors, increasing drug penetration promotes their regression and improves the therapeutic index of compounds. However, the heterogeneous extracellular matrix (ECM) acts as a steric and interaction barrier that hinders effective transport of therapeutics, including nanomedicines. Specifically, the interactions between the ECM and surface physicochemical properties of nanomedicines (e.g. charge, hydrophobicity) affect their diffusion and penetration. To address the challenges using existing surface chemistries, we used peptide-presenting phage libraries as a high-throughput approach to screen and identify peptides as coatings with desired physicochemical properties that improve diffusive transport through the tumor microenvironment. Through iterative screening against the ECM and identification by next-generation DNA sequencing and analysis, we selected individual clones and quantify their transport by diffusion assays. Here, we identified a net-neutral charge, hydrophilic peptide P4 that facilitates significantly higher diffusive transport of phage than negative control through in vitro tumor ECM. Through alanine mutagenesis, we confirmed that the hydrophilicity, charge, and spatial ordering impact diffusive transport. The P4 phage clone exhibited almost 200-fold improved uptake in ex vivo pancreatic tumor xenografts compared to the negative control. Nanoparticles coated with P4 exhibited ∼40-fold improvement in diffusivity in pancreatic tumor tissues, and P4-coated particles demonstrated less hindered diffusivity through the ECM compared to functionalized control particles. By leveraging the power of molecular diversity using phage display, we can greatly expand the chemical space of surface chemistries that can improve the transport of nanomedicines through the complex tumor microenvironment to ultimately improve their efficacy.
Persister cells resuscitate via ribosome modification by 23S rRNA pseudouridine synthase RluD

Upon a wide range of stress conditions (e.g. nutrient, antibiotic, oxidative), a subpopulation of bacterial cells known as persisters survives by halting metabolism. These cells resuscitate rapidly to reconstitute infections once the stress is removed and nutrients are provided. However, how these dormant cells resuscitate is not understood well but involves reactivating ribosomes. By screening 10,000 compounds directly for stimulating Escherichia coli persister cell resuscitation, we identified that 2-{[2-(4-bromophenyl)-2-oxoethyl]thio}-3-ethyl-5,6,7,8-tetrahydro[1]benzothieno[2,3-d]pyrimidin-4(3H)-one (BPOET) stimulates resuscitation. Critically, by screening 4267 E. coli proteins, we determined that BPOET activates hibernating ribosomes via 23S rRNA pseudouridine synthase RluD, which increases ribosome activity. Corroborating the increased waking with RluD, production of RluD increased the number of active ribosomes in persister cells. Also, inactivating the small RNA RybB which represses rluD led to faster persister resuscitation. Hence, persister cells resuscitate via activation of RluD.
A public good game with technological growth

Abstract We conduct a public good game with technological growth. In our endogenous growth treatment, subjects invest in the public good production technology (“contribution productivity”) and make contributions to provision. In the exogenous growth treatment, we increase contribution productivity at a rate that matches the growth in the endogenous growth treatment. We also have a control treatment in which there is no growth. With endogenous growth, subjects initially invest high and contribute low amounts, and shift their expenditure from investment to contributions as the rounds proceed. In a second sequence of rounds, average earnings are higher than in the first sequence. While the patterns of contributions are generally similar in the exogenous growth treatment, it is only with endogenous growth that average earnings are higher in the second sequence than in the first.
EIT Crinkles as Evidence for the Breakout Model of Solar Eruptions

We present observations of two homologous flares in NOAA active region 8210 occurring on 1998 May 1 and May 2, using EUV data from the Extreme Ultraviolet Radiation Imaging Telescope (EIT) on the Solar and Heliospheric Observatory (SOHO), high-resolution and high-time cadence images from the soft X-ray telescope (SXT) on Yohkoh, images or fluxes from the hard X-ray telescope (HXT) on Yohkoh and the BATSE experiment on the Compton Gamma Ray Observatory (CGRO), and Ca xix soft X-ray spectra from the Bragg crystal spectrometer (BCS) on Yohkoh. Magnetograms indicate that the flares occurred in a complex magnetic topology, consisting of an emerging flux region (EFR) sandwiched between a sunspot to the west and a coronal hole to the east. In an earlier study we found that in EIT images, both flaring episodes showed the formation of a crinkle-like pattern of emission ("EIT crinkles") occurring in the coronal hole vicinity, well away from a central "core field" area near the EFR-sunspot boundary. With our expanded data set, here we find that most of the energetic activity occurs in the core region in both events, with some portions of the core brightening shortly after the onset of the EIT crinkles, and other regions of the core brightening several minutes later, coincident with a burst of hard X-rays: there are no obvious core brightenings prior to the onset of the EIT crinkles. These timings are consistent with the "breakout model" of solar eruptions, whereby the emerging flux is initially constrained by a system of overlying magnetic field lines, and is able to erupt only after an opening develops in the overlying fields as a consequence of magnetic reconnection at a magnetic null point. In our case, the EIT crinkles would be a signature of this pre-impulsive-phase magnetic reconnection, and brightening of the core only occurs after the core fields begin to escape through the newly-created opening in the overlying fields. Morphology in soft X-ray images and properties in hard X-rays differ between the two events, with complexities that preclude a simple determination of the dynamics in the core at the times of eruption. From the BCS spectra, we find that the core region expends energy at a rate of approx. 10(exp 26) erg per second during the time of the growth of the EIT crinkles; this rate is an upper limit to energy expended in the reconnections opening the overlying fields. Energy losses occur at an order-of-magnitude higher rate near the time of the peak of the events. There is little evidence of asymmetry in the spectra, consistent with the majority of the mass flows occurring normal to the line-of-sight. Both events have similar electron temperature dependencies on time.
Tissue-Resident Memory T Cells Mediate Immune Homeostasis in the Human Pancreas through the PD-1/PD-L1 Pathway.

Summary Non-recirculating tissue-resident memory T cells (TRMs) are the predominant T cell subset in diverse tissue sites, where they mediate protective immune responses in situ. Here, we reveal a role for TRM in maintaining immune homeostasis in the human pancreas through interactions with resident macrophages and the PD-1/PD-L1 inhibitory pathway. Using tissues obtained from organ donors, we identify that pancreas T cells comprise CD8+PD-1hi TRMs, which are phenotypically, functionally, and transcriptionally distinct compared to TRMs in neighboring jejunum and lymph node sites. Pancreas TRMs cluster with resident macrophages throughout the exocrine areas; TRM effector functions are enhanced by macrophage-derived co-stimulation and attenuated by the PD-1/PD-L1 pathways. Conversely, in samples from chronic pancreatitis, TRMs exhibit reduced PD-1 expression and reduced interactions with macrophages. These findings suggest important roles for PD-1 and TRM-macrophage interactions in controlling tissue homeostasis and immune dysfunctions underlying inflammatory disease, with important implications for PD-1-based immunotherapies.
Aluminum Abundances on the Red Giant Branch: Testing Helium Mixing as a Second Parameter

Although globular cluster horizontal-branch (HB) morphology is determined primarily by the cluster metallicity, observations have shown that clusters with similar metallicities sometimes have very different HB’s, indicating the existence of a second parameter. Sweigart (1997a, b) suggested that deep mixing on the red giant branch (RGB) may be a second parameter if He is mixed into the stellar envelope. A He-mixed RGB star would evolve onto the HB at a bluer color and brighter luminosity than an unmixed star. Cavallo et al. (1998) showed that Al is produced in large quantities only inside the H-burning shell where He is synthesized, so that Al makes a good tracer of He. Thus, a relationship should exist between the ratio of Al-rich to Al-normal stars on the RGB and the ratio of blue to red HB stars. Since He cannot be directly measured in cool giants, Al enhancements can be used as an indicator of He mixing.
A refined Poisson summation formula for certain Braverman-Kazhdan spaces

Braverman and Kazhdan (2000) introduced influential conjectures aimed at generalizing the Fourier transform and the Poisson summation formula. Their conjectures should imply that quite general Langlands L-functions have meromorphic continuations and functional equations as predicted by Langlands’ functoriality conjecture. As an evidence for their conjectures, Braverman and Kazhdan (2002) considered a setting related to the so-called doubling method in a later paper and proved the corresponding Poisson summation formula under restrictive assumptions on the functions involved. The connection between the two papers is made explicit in the work of Li (2018). In this paper, we consider a special case of the setting in Braverman and Kazhdan’s later paper and prove a refined Poisson summation formula that eliminates the restrictive assumptions of that paper. Along the way we provide analytic control on the Schwartz space we construct; this analytic control was conjectured to hold (in a slightly different setting) in the work of Braverman and Kazhdan (2002).
Node-Aware Improvements to Allreduce

The MPI_Allreduce collective operation is a core kernel of many parallel codebases, particularly for reductions over a single value per process. The commonly used allreduce recursive-doubling algorithm obtains the lower bound message count, yielding optimality for small reduction sizes based on node-agnostic performance models. However, this algorithm yields duplicate messages between sets of nodes. Node-aware optimizations in MPICH remove duplicate messages through use of a single master process per node, yielding a large number of inactive processes at each inter-node step. In this paper, we present an algorithm that uses the multiple processes available per node to reduce the maximum number of inter-node messages communicated by a single process, improving the performance of allreduce operations, particularly for small message sizes.
Concatenation of Scales Below 1 eV

There are (at least) four numbers of physical and cosmological significance, whose inferred values, when expressed in mass units, cluster in a window below 1 eV. There are: the neutrino mass, the neutrino chemical potential, the cosmological constant, and the size of two extra dimensions (if the fundamental scale of gravity is 1–10 TeV). In this note, we imagine ways in which these four numbers could all be connected.
Learning from satisfying assignments under continuous distributions

What kinds of functions are learnable from their satisfying assignments? Motivated by this simple question, we extend the framework of [DDS15], which studied the learnability of probability distributions over {0, 1}n defined by the set of satisfying assignments to "low-complexity" Boolean functions, to Boolean-valued functions defined over continuous domains. In our learning scenario there is a known "background distribution" D over Rn (such as a known normal distribution or a known log-concave distribution) and the learner is given i.i.d. samples drawn from a target distribution Df, where Df is D restricted to the satisfying assignments of an unknown low-complexity Boolean-valued function f. The problem is to learn an approximation D' of the target distribution Df which has small error as measured in total variation distance. We give a range of efficient algorithms and hardness results for this problem, focusing on the case when f is a low-degree polynomial threshold function (PTF). When the background distribution D is log-concave, we show that this learning problem is efficiently solvable for degree-1 PTFs (i.e., linear threshold functions) but not for degree-2 PTFs. In contrast, when D is a normal distribution, we show that this learning problem is efficiently solvable for degree-2 PTFs but not for degree-4 PTFs. Our hardness results rely on standard assumptions about secure signature schemes.
An Institutional Approach to Gender Diversity and Firm Performance

This study examines data from 35 countries and 24 industries to understand the relationship between gender diversity and firm performance. Previous studies report conflicting evidence: some find th...
Pricing and Quality Provision in a Supply Relationship: A Model of Efficient Relational Contracts

This paper develops a model in which a supply relationship can sustain higher effort levels if firms use cross-subsidized pricing.
Is NY's Supply-side Experiment Working? A Preliminary Analysis using County Unemployment Rates

The State of New York recently enacted business tax reforms. The first legislative act launched the START-UP NY program in 2014. It created tax free enterprise zones throughout the state to incentivize business incubation within, or relocation of existing firms to, the State of New York. In that same year, the state lowered its corporate tax rate state-wide from 7.1% to 6.5% in 2016. We use a difference-in-differences (DID) methodology, evaluated using county-level data, to empirically test whether New York’s recent business tax reforms significantly reduce unemployment, beyond what would exist in the absence of the reforms. We fail to find significant evidence that START-UP NY affects unemployment during the period studied, 2014-2017. We do, however, find evidence suggesting that New York lowering its corporate tax rates in 2016 is associated with a large reduction in unemployment (by approximately 90,000 jobs) in 2016 and a smaller reduction (by approximately 25,000 jobs) in 2017.
Neighborhood-Based Information Costs

We propose a new measure of the cost of information structures in rational inattention problems, the "neighborhood-based" cost functions, given that many applications involve states with a topological structure. These cost functions summarize the results of a sequential information sampling problem, and also capture a notion of perceptual distance. This second property allows neighborhood-based cost functions, unlike mutual information, to make accurate predictions about behavior in perceptual experiments. We compare the implications of our neighborhood-based cost functions with those of a mutual-information cost function in a series of applications: security design, global games, modeling perceptual judgments, and linear-quadratic-Gaussian problems.
On Positive Sasakian Geometry

A Sasakian structure \(\mathcal{S}\)=(\xi,\eta,\Phi,g) on a manifold Mis called positiveif its basic first Chern class c1(\(\mathcal{F}\)ξ) can be represented by a positive (1,1)-form with respect to its transverse holomorphic CR-structure. We prove a theorem that says that every positive Sasakian structure can be deformed to a Sasakian structure whose metric has positive Ricci curvature. This provides us with a new technique for proving the existence of positive Ricci curvature metrics on certain odd dimensional manifolds. As an example we give a completely independent proof of a result of Sha and Yang that for every nonnegative integer kthe 5-manifolds k#(S2×S3) admits metrics of positive Ricci curvature.
On algorithms for and computing with the tensor ring decomposition

Tensor decompositions such as the canonical format and the tensor train format have been widely utilized to reduce storage costs and operational complexities for high-dimensional data, achieving linear scaling with the input dimension instead of exponential scaling. In this paper, we investigate even lower storage-cost representations in the tensor ring format, which is an extension of the tensor train format with variable end-ranks. Firstly, we introduce two algorithms for converting a tensor in full format to tensor ring format with low storage cost. Secondly, we detail a rounding operation for tensor rings and show how this requires new definitions of common linear algebra operations in the format to obtain storage-cost savings. Lastly, we introduce algorithms for transforming the graph structure of graph-based tensor formats, with orders of magnitude lower complexity than existing literature. The efficiency of all algorithms is demonstrated on a number of numerical examples, and in certain cases, we demonstrate significantly higher compression ratios when compared to previous approaches to using the tensor ring format.
Generative Adversarial Networks for Distributed Intrusion Detection in the Internet of Things

To reap the benefits of the Internet of Things (IoT), it is imperative to secure the system against cyber attacks in order to enable mission critical and real-time applications. To this end, intrusion detection systems (IDSs) have been widely used to detect anomalies caused by a cyber attacker in IoT systems. However, due to the large-scale nature of the IoT, its IDS must operate in a distributed manner with minimum dependence on a central controller. Moreover, in many scenarios such as health and financial applications, the IoT application datasets are private and IoT devices (IoTDs) may not intend to share such data. To this end, in this paper, a distributed generative adversarial network (GAN) is proposed to provide a fully distributed IDS for the IoT so as to detect anomalous behavior without reliance on any centralized controller. In this architecture, every IoTD can monitor its own data as well as neighboring IoTDs to detect internal and external attacks. In addition, the proposed distributed IDS does not require any sharing of datasets among the IoTDs and, thus, it can be implemented in IoT applications that must preserve the privacy of user data such as health monitoring or financial applications. It is shown analytically that the proposed distributed GAN has higher accuracy of detecting intrusion compared to a standalone IDS that has access to only a single IoTD dataset. Simulation results show that the proposed distributed GAN-based IDS has up to 20% higher accuracy, 25% higher precision, and 60% lower false positive rate compared to a standalone GAN-based IDS.
Regularity of minimizers of a tensor-valued variational obstacle problem in three dimensions

Motivated by Ball and Majumdar’s modification of Landau-de Gennes model for nematic liquid crystals, we study energy-minimizer Q of a tensor-valued variational obstacle problem in a bounded 3-D domain with prescribed boundary data. The energy functional is designed to blow up as Q approaches the obstacle. Under certain assumptions, especially on blow-up profile of the singular bulk potential, we prove higher interior regularity of Q, and show that the contact set of Q is either empty, or small with characterization of its Hausdorff dimension. We also prove boundary partial regularity of the energy-minimizer.
Synthesis of nanoparticles in carbon arc: measurements and modeling

This work presents a study of the region of nanoparticle growth in an atmospheric pressure carbon arc. The nanoparticles are detected using the planar laser-induced incandescence technique. The measurements revealed large clouds of nanoparticles in the arc periphery bordering the region with a high density of diatomic carbon molecules. Two-dimensional computational fluid dynamic simulations of the arc combined with thermodynamic modeling show that this is due to the interplay of the condensation of carbon molecular species and the convection flow pattern. These results show that the nanoparticles are formed in the colder, peripheral regions of the arc and describe the parameters necessary for coagulation.
Mathematical Quantum Yang—Mills Theory Revisited II: Mass without Mass

Massless Dirac equation for spinor multiplets is minimally coupled with a unitary representation of an arbitrary compact semisimple gauge group. The spectrum of the quantized coupled Dirac Hamiltonian has a positive mass gap running along the classical energy scale.
Electrical Conductivity in a Porous, Cubic Rare-Earth Catecholate

Electrically conductive metal–organic frameworks (MOFs) provide a rare example of porous materials that can efficiently transport electrical current, a combination that is favorable for a variety o...
Reliable Detection of Unknown Cell-Edge Users via Canonical Correlation Analysis

Providing reliable service to users close to the edge between cells remains a challenge in cellular systems, even as 5G deployment is around the corner. These users are subject to significant signal attenuation, which also degrades their uplink channel estimates. Even joint detection using base station (BS) cooperation often fails to reliably detect such users, due to near-far power imbalance, and channel estimation errors. Is it possible to bypass the channel estimation stage and design a detector that can reliably detect cell-edge user signals under significant near-far imbalance? This paper shows, perhaps surprisingly, that the answer is affirmative – albeit not via traditional multiuser detection. Exploiting that cell-edge user signals are weak but common to different base stations, while cell-center users are unique to their serving BS, this paper establishes an elegant connection between cell-edge user detection and canonical correlation analysis (CCA) of the associated space-time baseband-equivalent matrices. It proves that CCA identifies the common subspace of these matrices, even under significant intra-and inter-cell interference. The resulting mixture of cell-edge user signals can subsequently be unraveled using a well-known algebraic signal processing technique. Interestingly, the proposed approach does not even require that the signals from the different base stations are synchronized – the right synchronization can be automatically determined as well. Experimental results demonstrate that the proposed approach achieves order of magnitude BER improvements compared to ‘oracle’ multiuser detection that assumes perfect knowledge of the cell-center user channels.
The Informational Role of Buyback Contracts

Manufacturers often offer retailers buyback contracts to reduce retailers’ inventory costs by repurchasing unsold inventory at a prespecified returns price. We examine the signaling role of buyback...
Chapter 11 – DSP System Design: Software-Defined Radio

Publisher Summary This chapter describes the software-defined radio digital signal processing (DSP) system built using LabVIEW. A software-defined radio essentially consists of a programmable communication system where functional changes can be made by simply updating the software. 4-Quadrature Amplitude Modulation (QAM) scheme is often used for software-defined radio system. This modulation scheme is widely used for data transmission applications over bandpass channels such as FAX modem, high speed cable, multi-tone wireless, and satellite systems. For transmission in a QAM transmitter, pseudo noise (PN) sequences are generated to serve as the message signal. A PN sequence is often generated with a five-stage linear feedback shift register structure. Frame synchronization is required for properly grouping transmitted bits into an alphabet. In order to achieve this synchronization, a similarity measure, consisting of cross correlation is often computed between the known marker bits and received samples.
Neural Plasticity of Spinal Reflexes

Spinal reflexes have long been considered hard wired, essentially unchanging input–output relationships. Work over the past 25–30 years has shown that the spinal reflex circuits are continually undergoing functional and perhaps structural changes that may be important in nociception (pain) processing, locomotor patterns, sensory information control, and more. Nonassociative plasticity involves a series of processes ranging from shortlasting to essentially permanent excitability changes in reflex function. Associative plasticity includes classically conditioned, instrumentally conditioned, and operantly conditioned alterations. The driving force behind many of these alterations is nociceptive input, but non-nociceptive inputs are also important. These findings have implications for human function and rehabilitation from spinal injuries.
Chapter 1 – A Historical Perspective on Clinical Research

Publisher Summary The spectacular advances in medicine during the 20th century would never have happened without the centuries of earlier progress. In the 20th century, medical colleges became well established in Europe and the United States. The great contributions of medicine in the 20th century are linked to the early commitment to strong medical education. This chapter provides historical highlights, which gives a perspective of the continuum of the clinical research endeavor. The account includes discussions on the earliest clinical research, the Greek and Roman influence, Middle Ages and Renaissance, as well as advances in medicine in the 17th, 18th, 19th, 20th centuries and beyond. In the past 50 years, clinical research has become big business. The pharmaceutical and the biotechnology industries have engaged university-based clinical investigators in the business of clinical research. The relationship between industry and academia grew closer, and new ethical, legal, and social issues evolved. The opportunities for doing clinical research in the 21st century are greater than ever. Today, understanding and meeting public concern are as important for the clinical investigator as performing the clinical study. Principles for conducting clinical research have evolved from centuries of experience. As the science moves forward, ethical, legal, and social issues pose special challenges for the clinical investigator.
10 – Radiation detectors†

Publisher Summary This chapter describes the important features of detectors. Most of them are based on the ionization produced by incoming radiation. The detector operates in two modes are current and pulse. In current, the average electric flow is measured by ammeter and in pulse, the electrical signals produced by individual particles or rays are amplified and counted. A detector operating in this mode is known as a counter. The detection of radiation and the measurement of its properties are required in all aspects of the nuclear field. In gas counters, the ionization produced by incoming radiation is collected. Dependent on the voltage between electrodes, counters detect all particles or distinguish between types of particles. Neutrons are detected indirectly by the products of nuclear reactions, for slow neutrons by absorption in boron or uranium-235, for fast neutrons by scattering in hydrogen. Scintillation counters release measurable light upon bombardment by charged particles or gamma rays. Solid-state detectors generate a signal from the motion of electron–hole pairs created by ionizing radiation. Pulse–height analysis yields energy distributions of particles. Statistical methods are employed to estimate the uncertainty in measured counting rates. Advanced specialized detectors are used in high-energy physics research.
Gramicidin Pores Report the Activity of Membrane-Active Enzymes

Phospholipases constitute a ubiquitous class of membrane-active enzymes that play a key role in cellular signaling, proliferation, and membrane trafficking. Aberrant phospholipase activity is implicated in a range of diseases including cancer, inflammation, and myocardial disease. Characterization of these enzymes is therefore important, both for improving the understanding of phospholipase catalysis and for accelerating pharmaceutical and biotechnological applications. This paper describes a novel approach to monitor, in situ and in real-time, the activity of phospholipase D (PLD) and phospholipase C (PLC) on planar lipid bilayers. This method is based on lipase-induced changes in the electrical charge of lipid bilayers and on the concomitant change in ion concentration near lipid membranes. The approach reports these changes in local ion concentration by a measurable change in the single channel ion conductance through pores of the ion channel-forming peptide gramicidin A. This enzyme assay takes advant...
2 – Atoms and nuclei

Publisher Summary This chapter discusses the atomic and nuclear theory of molecules. The most elementary concept is that matter is composed of individual particles known as atoms that retain their identity as elements in ordinary physical and chemical interactions. All material is composed of elements whose chemical interaction depends on the number of electrons, Z. Light is absorbed and emitted in the form of photons when atomic electrons jump between orbits. Isotopes of elements differ according to the number of nucleons A. To distinguish isotopes, one can identify the mass number A, as the total number of nucleons and the heavy particles in the nucleus. Nuclei are much smaller than atoms and contain most of the mass of the atom. The nucleons are bound together by a net force in which the nuclear attraction forces exceed the electrostatic repulsion forces. Energy must be supplied to dissociate a nucleus into its components.
Refugee or Internally Displaced Person: To Where Should One Flee

This study investigates the circumstances that lead some countries to produce a large number of refugees and relatively few internally displaced persons (IDPs) as opposed to a large number of IDPs ...
The Pricing of Tail Risk and the Equity Premium: Evidence From International Option Markets

We explore the pricing of tail risk as manifest in index options across international equity markets. The risk premium associated with negative tail events displays persistent shifts, unrelated to ...