cssi/SciSciGPT-SciSciNet huggingface dataset:
Diffusive Phonons in Nongray Nanostructures

Nanostructured semiconducting materials are promising candidates for thermoelectrics due to their potential to suppress phonon transport while preserving electrical properties. Modeling phonon-boundary scattering in complex geometries is crucial for predicting materials with high conversion efficiency. However, the simultaneous presence of ballistic and diffusive phonons challenges the development of models that are both accurate and computationally tractable. Using the recently developed first-principles Boltzmann transport equation (BTE) approach, we investigate diffusive phonons in nanomaterials with wide mean-free-path (MFP) distributions. First, we derive the short MFP limit of the suppression function, showing that it does not necessarily recover the value predicted by standard diffusive transport, challenging previous assumptions. Second, we identify a Robin type boundary condition describing diffuse surfaces within Fourier's law, extending the validity of diffusive heat transport in terms of Knudsen numbers. Finally, we use this result to develop a hybrid Fourier/BTE approach to model realistic materials, obtaining excellent agreement with experiments. These results provide insight on thermal transport in materials that are within experimental reach and open opportunities for large-scale screening of nanostructured thermoelectric materials.
True randomness from an incoherent source

Quantum random number generators (QRNGs) harness the intrinsic randomness in measurement processes: the measurement outputs are truly random, given the input state is a superposition of the eigenstates of the measurement operators. In the case of trusted devices, true randomness could be generated from a mixed state ρ so long as the system entangled with ρ is well protected. We propose a random number generation scheme based on measuring the quadrature fluctuations of a single mode thermal state using an optical homodyne detector. By mixing the output of a broadband amplified spontaneous emission (ASE) source with a single mode local oscillator (LO) at a beam splitter and performing differential photo-detection, we can selectively detect the quadrature fluctuation of a single mode output of the ASE source, thanks to the filtering function of the LO. Experimentally, a quadrature variance about three orders of magnitude larger than the vacuum noise has been observed, suggesting this scheme can tolerate much higher detector noise in comparison with QRNGs based on measuring the vacuum noise. The high quality of this entropy source is evidenced by the small correlation coefficients of the acquired data. A Toeplitz-hashing extractor is applied to generate unbiased random bits from the Gaussian distributed raw data, achieving an efficiency of 5.12 bits per sample. The output of the Toeplitz extractor successfully passes all the NIST statistical tests for random numbers.
Defining, Conceptualising and Measuring the Digital Economy

The digital economy is growing fast, especially in developing countries. Yet the meaning and metrics of the digital economy are both limited and divergent. The aim of this paper is to review what is currently known in order to develop a definition of the digital economy, and an estimate of its size. The paper argues there are three scopes of relevance. The core of the digital economy is the ‘digital sector’: the IT/ICT sector producing foundational digital goods and services. The true ‘digital economy’ - defined as “that part of economic output derived solely or primarily from digital technologies with a business model based on digital goods or services” - consists of the digital sector plus emerging digital and platform services. The widest scope - use of ICTs in all economic fields - is here referred to as the ‘digitalised economy’. Following a review of measurement challenges, the paper estimates the digital economy as defined here to make up around 5% of global GDP and 3% of global employment. Behind this lies significant unevenness: the global North has had the lion’s share of the digital economy to date, but growth rates are fastest in the global South. Yet potential growth could be much higher: further research to understand more about the barriers to and impacts of the digital economy in developing countries is therefore a priority.
Low-Cloud Feedbacks from Cloud-Controlling Factors: A Review

The response to warming of tropical low-level clouds including both marine stratocumulus and trade cumulus is a major source of uncertainty in projections of future climate. Climate model simulations of the response vary widely, reflecting the difficulty the models have in simulating these clouds. These inadequacies have led to alternative approaches to predict low-cloud feedbacks. Here, we review an observational approach that relies on the assumption that observed relationships between low clouds and the “cloud-controlling factors” of the large-scale environment are invariant across time-scales. With this assumption, and given predictions of how the cloud-controlling factors change with climate warming, one can predict low-cloud feedbacks without using any model simulation of low clouds. We discuss both fundamental and implementation issues with this approach and suggest steps that could reduce uncertainty in the predicted low-cloud feedback. Recent studies using this approach predict that the tropical low-cloud feedback is positive mainly due to the observation that reflection of solar radiation by low clouds decreases as temperature increases, holding all other cloud-controlling factors fixed. The positive feedback from temperature is partially offset by a negative feedback from the tendency for the inversion strength to increase in a warming world, with other cloud-controlling factors playing a smaller role. A consensus estimate from these studies for the contribution of tropical low clouds to the global mean cloud feedback is 0.25 ± 0.18 W m−2 K−1 (90% confidence interval), suggesting it is very unlikely that tropical low clouds reduce total global cloud feedback. Because the prediction of positive tropical low-cloud feedback with this approach is consistent with independent evidence from low-cloud feedback studies using high-resolution cloud models, progress is being made in reducing this key climate uncertainty.
Bayesian Diallel Analysis Reveals Mx1-Dependent and Mx1-Independent Effects on Response to Influenza A Virus in Mice

Influenza A virus (IAV) is a respiratory pathogen that causes substantial morbidity and mortality during both seasonal and pandemic outbreaks. Infection outcomes in unexposed populations are affected by host genetics, but the host genetic architecture is not well understood. Here, we obtain a broad view of how heritable factors affect a mouse model of response to IAV infection using an 8 × 8 diallel of the eight inbred founder strains of the Collaborative Cross (CC). Expanding on a prior statistical framework for modeling treatment response in diallels, we explore how a range of heritable effects modify acute host response to IAV through 4 d postinfection. Heritable effects in aggregate explained ∼57% of the variance in IAV-induced weight loss. Much of this was attributable to a pattern of additive effects that became more prominent through day 4 postinfection and was consistent with previous reports of antiinfluenza myxovirus resistance 1 (Mx1) polymorphisms segregating between these strains; these additive effects largely recapitulated haplotype effects observed at the Mx1 locus in a previous study of the incipient CC, and are also replicated here in a CC recombinant intercross population. Genetic dominance of protective Mx1 haplotypes was observed to differ by subspecies of origin: relative to the domesticus null Mx1 allele, musculus acts dominantly whereas castaneus acts additively. After controlling for Mx1, heritable effects, though less distinct, accounted for ∼34% of the phenotypic variance. Implications for future mapping studies are discussed.
Point mutation in D8C domain of Tamm-Horsfall protein/uromodulin in transgenic mice causes progressive renal damage and hyperuricemia.

Hereditary mutations in Tamm-Horsfall protein (THP/uromodulin) gene cause autosomal dominant kidney diseases characterized by juvenile-onset hyperuricemia, gout and progressive kidney failure, although the disease pathogenesis remains unclear. Here we show that targeted expression in transgenic mice of a mutation within the domain of 8 cysteines of THP in kidneys’ thick ascending limb (TAL) caused unfolded protein response in younger (1-month old) mice and apoptosis in older (12-month old) mice. While the young mice had urine concentration defects and polyuria, such defects progressively reversed in the older mice to marked oliguria, highly concentrated urine, fibrotic kidneys and reduced creatinine clearance. Both the young and the old transgenic mice had significantly higher serum uric acid and its catabolic product, allantoin, than age-matched wild-type mice. This THP mutation apparently caused primary defects in TAL by compromising the luminal translocation and reabsorptive functions of NKCC2 and ROMK and secondary responses in proximal tubules by upregulating NHE3 and URAT1. Our results strongly suggest that the progressive worsening of kidney functions reflects the accumulation of the deleterious effects of the misfolded mutant THP and the compensatory responses. Transgenic mice recapitulating human THP/uromodulin-associated kidney diseases could be used to elucidate their pathogenesis and test novel therapeutic strategies.
Early Vaccine-type Pneumococcal Acute Otitis Media Does not Predispose to Subsequent Otitis When Compared With Early Acute Otitis Media Due to Other Bacterial Etiology

Background:Prevention of acute otitis media (AOM), and especially recurrence and biofilm formation, by pneumococcal conjugate vaccines has been hypothesized to be due to prevention of early episodes triggering the vicious cycle. We tested the specific role of vaccine-type pneumococcal AOM in this hy
Recent research developments of strategic consumer behavior in operations management

Abstract Strategic consumer behavior has been extensively studied in the Management Science and Operations Management community. We survey recent developments in the literature and review possible operational strategies and decisions to counteract the adverse impact of strategic consumer behavior. Specifically, we broadly characterize these decisions into three classes – Pricing, Inventory, and Information – and further discuss the influence of strategic consumer behavior on these decisions and their underlying mechanisms on counteracting consumers’ strategic waiting behavior.
The NEAT Predictive Model for Survival in Patients with Advanced Cancer.

PURPOSE We previously developed a model to more accurately predict life expectancy for stage IV cancer patients referred to radiation oncology. The goals of this study are to validate this model and to compare competing published models. Materials and Methods From May 2012 to March 2015, 280 consecutive patientswith stage IV cancerwere prospectively evaluated by a single radiation oncologist. Patients were separated into training, validation and combined sets. TheNEAT model evaluated number of active tumors ("N"), Eastern Cooperative Oncology Group performance status ("E"), albumin ("A") and primary tumor site ("T"). The Odette Cancer Center model validated performance status, bone only metastases and primary tumor site. The Harvard TEACHH model investigated primary tumor type, performance status, age, prior chemotherapy courses, liver metastases, and hospitalization within 3 months. Cox multivariable analyses and logisticalregressionwere utilized to compare model performance. RESULTS Number of active tumors, performance status, albumin, primary tumor site, prior hospitalizationwithin the last 3 months, and liver metastases predicted overall survival on uinvariate and multivariable analysis (p < 0.05 for all). The NEAT model separated patients into four prognostic groups with median survivals of 24.9, 14.8, 4.0, and 1.2 months, respectively (p < 0.001). The NEAT model had a C-index of 0.76 with a Nagelkerke's R2 of 0.54 suggesting good discrimination, calibration and total performance compared to competing prognostic models. CONCLUSION The NEAT model warrants further investigation as a clinically useful approach to predict survival in patients with stage IV cancer.
Localized recombination drives diversification of killing spectra for phage-derived syringacins

To better understand the potential for antagonistic interactions between members of the same bacterial species, we have surveyed bacteriocin killing activity across a diverse suite of strains of the phytopathogen Pseudomonas syringae. Our data demonstrate that killing activity from phage-derived bacteriocins of P. syringae (R-type syringacins) is widespread. Despite a high overall diversity of bacteriocin activity, strains can broadly be classified into five main killing types and two main sensitivity types. Furthermore, we show that killing activity switches frequently between strains and that switches correlate with localized recombination of two genes that together encode the proteins that specify bacteriocin targeting. Lastly, we demonstrate that phage-derived bacteriocin killing activity can be swapped between strains simply through expression of these two genes in trans. Overall, our study characterizes extensive diversity of killing activity for phage-derived bacteriocins of P. syringae across strains and highlights the power of localized recombination to alter phenotypes that mediate strain interactions during evolution of natural populations and communities.
Do management interventions last ? evidence from India

Beginning in 2008, the authors conducted a randomized controlled trial that changed management practices in a set of Indian weaving firms (Bloom et al. 2013). In 2017 the plants were revisited and the authors found three main results. First, while about half of the management practices adopted in the original experimental plants had been dropped, there was still a large and significant gap in practices between the treatment and control plants. Likewise, there remained a significant performance gap between treatment and control plants, suggesting lasting impacts of effective management interventions. Second, while few management practices had demonstrably spread across the firms in the study, many had spread within firms, from the experimental plants to the non-experimental plants, suggesting limited spillovers between firms but large spillovers within firms. Third, managerial turnover and the lack of director time were two of the most cited reasons for the drop in management practices in experimental plants, highlighting the importance of key employees.
Quantum photonics model for nonclassical light generation using integrated nanoplasmonic cavity-emitter systems

The implementation of nonclassical light sources is becoming increasingly important for various quantum applications. A particularly interesting approach is to integrate such functionalities on a single chip as this could pave the way towards fully scalable quantum photonic devices. Several approaches using dielectric systems have been investigated in the past. However, it is still not understood how on-chip nanoplasmonic antennas, interacting with a single quantum emitter, affect the quantum statistics of photons reflected or transmitted in the guided mode of a waveguide. Here we investigate a quantum photonic platform consisting of an evanescently coupled nanoplasmonic cavity-emitter system and discuss the requirements for nonclassical light generation. We develop an analytical model that incorporates quenching due to the nanoplasmonic cavity to predict the quantum statistics of the transmitted and reflected guided waveguide light under weak coherent pumping. The analytical predictions match numerical simulations based on a master equation approach. It is moreover shown that for resonant excitation the degree of antibunching in transmission is maximized for an optimal cavity modal volume ${V}_{c}$ and cavity-emitter distance $s$. In reflection, perfectly antibunched light can only be obtained for specific $({V}_{c},s)$ combinations. Finally, our model also applies to dielectric cavities and as such can guide future efforts in the design and development of on-chip nonclassical light sources using dielectric and nanoplasmonic cavity-emitter systems.
Operating asymmetries and non-linear spline correction in discretionary accrual models

Earnings management research often uses discretionary accruals from Jones-type models. These models assume a linear relation between sales changes and accruals. However, we predict and find that sales changes have a non-linear asymmetric effect on accruals through managers’ operating decisions. By forcing a linear specification on this non-linear effect, the modified Jones model overestimates (underestimates) discretionary accruals for moderate (extreme) sales changes. This non-linear bias causes excessive type-I error in tests of positive (negative) discretionary accruals for subsamples with moderate (extreme) sales growth. The literature often controls for non-linearities using performance matching (Kothari et al. in J Account Econ 39(1):163–197, 2005). However, we show that this approach leads to false inferences due to matching on the dependent variable. We use a flexible non-linear spline specification to control for the non-linear operating effect of sales changes. Our method successfully mitigates the bias, improves type-I errors without sacrificing test power, and changes inferences about major prior findings.
Copa: Practical Delay-Based Congestion Control for the Internet.

Congestion control schemes that are commonly deployed today are loss-based and were developed in the 2000s. The internet has changed dramatically since then, and these schemes are no longer suitable. This has prompted new research interest in this area, ranging from complex machine learning and optimization techniques [1, 2, 3, 4] to exploring usage of hitherto under-explored ack-arrival rate as signals [5, 6, 7]. It has long been recognized that delay-based congestion control overcomes many challenges that loss-based schemes face [8, 9]. However they have challenges of their own that have precluded their deployment. In this work, we identify and solve some of these challenges to create Copa, a practical delay-based congestion control algorithm for the Internet. Copa solves three challenges. (1) It periodically empties the queue to accurately measure the minimum (base) RTT, (2) it proposes a simple estimator for queuing delay that is robust to noise in RTT measurements, (3) it introduces "TCP-mode switching": normally Copa maintains low delays, but typically delay-sensitive schemes get low throughput when a buffer-filling flow shares the bottleneck. To solve this problem, when Copa detects a non-Copa flow, such as a buffer-filling TCP, it switches to a TCP-competitive mode to get its fair share of the bandwidth. Based on work published at Arun, Venkat, and Hari Balakrishnan. "Copa: Practical Delay-Based Congestion Control for the Internet." 15th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 18). USENIX} Association, 2018.
Certified Computation from Unreliable Datasets

A wide range of learning tasks require human input in labeling massive data. The collected data though are usually low quality and contain inaccuracies and errors. As a result, modern science and business face the problem of learning from unreliable data sets. In this work, we provide a generic approach that is based on \textit{verification} of only few records of the data set to guarantee high quality learning outcomes for various optimization objectives. Our method, identifies small sets of critical records and verifies their validity. We show that many problems only need $\text{poly}(1/\varepsilon)$ verifications, to ensure that the output of the computation is at most a factor of $(1 \pm \varepsilon)$ away from the truth. For any given instance, we provide an \textit{instance optimal} solution that verifies the minimum possible number of records to approximately certify correctness. Then using this instance optimal formulation of the problem we prove our main result: "every function that satisfies some Lipschitz continuity condition can be certified with a small number of verifications". We show that the required Lipschitz continuity condition is satisfied even by some NP-complete problems, which illustrates the generality and importance of this theorem. In case this certification step fails, an invalid record will be identified. Removing these records and repeating until success, guarantees that the result will be accurate and will depend only on the verified records. Surprisingly, as we show, for several computation tasks more efficient methods are possible. These methods always guarantee that the produced result is not affected by the invalid records, since any invalid record that affects the output will be detected and verified.
Making Recommendations Bandwidth Aware

This paper asks how much we can gain in terms of bandwidth and user satisfaction, if recommendation systems became bandwidth aware and took into account not only the user preferences, but also the fact that they may need to serve these users under bandwidth constraints, as is the case over wireless networks. We formulate this as a new problem in the context of index coding: we relax the index coding requirements to capture scenarios where each client has preferences associated with messages. The client is satisfied to receive any message she does not already have, with a satisfaction proportional to her preference for that message. We consistently find, over a number of scenarios we sample, that although the optimization problems are in general NP-hard, significant bandwidth savings are possible even when restricted to polynomial time algorithms.
Large-amplitude Blazar Polarization Angle Swing as a Signature of Magnetic Reconnection

Relativistic magnetic reconnection events may exist in magnetized plasmas in astrophysical systems. During this process, oppositely directed magnetic field lines reconnect and release magnetic energy, efficiently accelerating nonthermal particles. However, so far there is little clear observational signatures of relativistic magnetic reconnection events in astrophysical systems. Blazars are relativistic magnetized plasma outflows from supermassive black holes. Their multi-wavelength flares may be powered by relativistic magnetic reconnection. The highly variable radiation and polarization signatures are well covered by multi-wavelength observation campaigns, making them ideal targets to examine the magnetic reconnection model. Recent observations have found that several blazar flares are accompanied by optical polarization angle swings that may have an amplitude as large as >180°, challenging existing theoretical models. In this Letter, we present integrated particle-in-cell and polarized radiation transfer simulations of magnetic reconnection events. We find that plasmoid coalescences in the reconnection layer can give rise to highly variable light curves, low and fluctuating polarization degree, and rotating polarization angle. In particular, large-amplitude polarization angle swings, similar to those observed during blazar flares, can be a unique signature of relativistic magnetic reconnection events.
Support Neighbor Loss for Person Re-Identification

Person re-identification (re-ID) has recently been tremendously boosted due to the advancement of deep convolutional neural networks (CNN). The majority of deep re-ID methods focus on designing new CNN architectures, while less attention is paid on investigating the loss functions. Verification loss and identification loss are two types of losses widely used to train various deep re-ID models, both of which however have limitations. Verification loss guides the networks to generate feature embeddings of which the intra-class variance is decreased while the inter-class ones is enlarged. However, training networks with verification loss tends to be of slow convergence and unstable performance when the number of training samples is large. On the other hand, identification loss has good separating and scalable property. But its neglect to explicitly reduce the intra-class variance limits its performance on re-ID, because the same person may have significant appearance disparity across different camera views. To avoid the limitations of the two types of losses, we propose a new loss, called support neighbor (SN) loss. Rather than being derived from data sample pairs or triplets, SN loss is calculated based on the positive and negative support neighbor sets of each anchor sample, which contain more valuable contextual information and neighborhood structure that are beneficial for more stable performance. To ensure scalability and separability, a softmax-like function is formulated to push apart the positive and negative support sets. To reduce intra-class variance, the distance between the anchor's nearest positive neighbor and furthest positive sample is penalized. Integrating SN loss on top of Resnet50, superior re-ID results to the state-of-the-art ones are obtained on several widely used datasets.
Scalable Manifold Learning for Big Data with Apache Spark

Non-linear spectral dimensionality reduction methods, such as Isomap, remain important technique for learning manifolds. However, due to computational complexity, exact manifold learning using Isomap is currently impossible from large-scale data. In this paper, we propose a distributed memory framework implementing end-to-end exact Isomap under Apache Spark model. We show how each critical step of the Isomap algorithm can be efficiently realized using basic Spark model, without the need to provision data in the secondary storage. We show how the entire method can be implemented using PySpark, offloading compute intensive linear algebra routines to BLAS. Through experimental results, we demonstrate excellent scalability of our method, and we show that it can process datasets orders of magnitude larger than what is currently possible, using a 25-node parallel cluster.
Data-Driven Identification of a Thermal Network in Multi-Zone Building

System identification of smart buildings is necessary for their optimal control and application in demand response. The thermal response of a building around an operating point can be modeled using a network of interconnected resistors with capacitors at each node/zone called RC network. The development of the RC network involves two phases: obtaining the network topology, and estimating thermal resistances and capacitance's. In this article, we present a provable method to reconstruct the interaction topology of thermal zones of a building solely from temperature measurements. We demonstrate that our learning algorithm accurately reconstructs the interaction topology for a 5 zone office building in EnergyPlus with real-world conditions. We show that our learning algorithm is able to recover the network structure in scenarios where prior research prove insufficient.
Unloader knee brace increases medial compartment joint space during gait in knee osteoarthritis patients.

The purpose of the present study was to investigate the effect of the unloader brace on medial compartment dynamic joint space (DJS) during gait, while simultaneously recording ground reaction force (GRF) in varus knee osteoarthritis (OA) patients using a highly accurate biplane radiography system which allowed continuous measurement of DJS from heel strike through the midstance phase of gait. The hypothesis was that DJS in the medial compartment would be greater with the unloader brace than without the brace during gait. After 2 weeks of daily use of the unloader brace, ten varus knee OA patients (age 52 ± 8 years) walked with and without the brace on an instrumented treadmill, while biplane radiographs of the OA knees were acquired at 100 Hz. Medial compartment DJS was determined from heel strike to terminal stance (0–40% of the gait cycle) using a validated volumetric model-based tracking process that matches subject-specific 3D bone models to the biplane radiographs. The GRF during gait was collected at 1000 Hz. Repeated-measures ANOVA was used to explore differences in medial compartment DJS and GRF between the unbraced and braced conditions. A patient-reported subjective questionnaire related to the brace use was collected at the time of the test. Medial compartment DJS was significantly greater with the unloader brace than without the brace during gait (P = 0.005). The average difference was 0.3 mm (95% confidence interval 0.1–0.4 mm). No significant difference was observed in terms of vertical GRF between the two conditions. The questionnaire showed participants felt reduced pain when wearing the brace. The unloader brace has the significant effect of increasing medial compartment DJS during gait, which supports the underlying premise that the unloader brace reduces pain by increasing medial joint space during dynamic loading activities. III.
Two Notions of Naturalness

My aim in this paper is twofold: (i) to distinguish two notions of naturalness employed in beyond the standard model (BSM) physics and (ii) to argue that recognizing this distinction has methodological consequences. One notion of naturalness is an “autonomy of scales” requirement: it prohibits sensitive dependence of an effective field theory’s low-energy observables on precise specification of the theory’s description of cutoff-scale physics. I will argue that considerations from the general structure of effective field theory provide justification for the role this notion of naturalness has played in BSM model construction. A second, distinct notion construes naturalness as a statistical principle requiring that the values of the parameters in an effective field theory be “likely” given some appropriately chosen measure on some appropriately circumscribed space of models. I argue that these two notions are historically and conceptually related but are motivated by distinct theoretical considerations and admit of distinct kinds of solution.
Comparing Different Preprocessing Methods in Automated Segmentation of Retinal Vasculature

Computer methods and image processing provide medical doctors assistance at any time and relieve their work load, especially for iterative processes like identifying objects of interest such as lesions and anatomical structures from the image. Vescular detection is considered to be a crucial step in some retinal image analysis algorithms to find other retinal landmarks and lesions, and their corresponding diameters, to use as a length reference to measure objects in the retina. The objective of this study is to compare effect of two preprocessing methods on retinal vessel segmentation methods, Laplacian-of-Gaussian edge detector (using second-order spatial differentiation), Canny edge detector (estimating the gradient intensity), and Matched filter edge detector either in the normal fundus images or in the presence of retinal lesions like diabetic retinopathy. The steps for the segmentation are as following: 1) Smoothing: suppress as much noise as possible, without destroying the true edges, 2) Enhancement: apply a filter to enhance the quality of the edges in the image (sharpening), 3) Detection: determine which edge pixels should be discarded as noise and which should be retained by thresholding the edge strength and edge size, 4) Localization: determine the exact location of an edge by edge thinning or linking. From the accuracy view point, comparing to manual segmentation performed by ophthalmologists for retinal images belonging to a test set of 120 images, by using first preprocessing method, Illumination equalization, and contrast enhancement, the accuracy of Canny, Laplacian-of-Gaussian, and Match filter vessel segmentation was more than 85% for all databases (MUMS-DB, DRIVE, MESSIDOR). The performance of the segmentation methods using top-hat preprocessing (the second method) was more than 80%. And lastly, using matched filter had maximum accuracy for the vessel segmentation for all preprocessing steps for all databases.
An efficient sequential learning algorithm in regime-switching environments

We provide a novel approach of estimating a regime-switching nonlinear and non-Gaussian state-space model based on a particle learning scheme. In particular, we extend the particle learning method in Liu, J., and M. West. 2001. “Combined Parameter and State Estimation in Simulation-Based Filtering.” In Sequential Monte Carlo Methods in Practice, 197–223. Springer. by constructing a new proposal distribution for the latent regime index variable that incorporates all available information contained in the current and past observations. The Monte Carlo simulation result implies that our approach categorically outperforms a popular existing algorithm. For empirical illustration, the proposed algorithm is used to analyze the underlying dynamics of US excess stock return.
Fragmented Division of Labor and Healthcare Costs: Evidence from Moves Across Regions

Abstract Policies aiming to improve healthcare productivity often focus on reducing care fragmentation. Care fragmentation occurs when services are spread across many providers, potentially making coordination difficult. Using Medicare claims data, we analyze the effect of moving to a region with more fragmented care delivery. We find that 60% of regional variation in care fragmentation is independent of patients' individual demand for care and moving to a region with 1 SD higher fragmentation increases care utilization by 10%. When patients move to more fragmented regions, they increase their use of specialists and have fewer encounters with primary care physicians. More fragmented regions have more intensive care provision on many margins, including services sometimes associated with overutilization (hospitalizations, emergency department visits, repeat imaging studies) as well as services associated with high value care (vaccines, guideline concordant for diabetics).
A simple approach to construct confidence bands for a regression function with incomplete data

A long-standing problem in the construction of asymptotically correct confidence bands for a regression function $$m(x)=E[Y|X=x]$$, where Y is the response variable influenced by the covariate X, involves the situation where Y values may be missing at random, and where the selection probability, the density function f(x) of X, and the conditional variance of Y given X are all completely unknown. This can be particularly more complicated in nonparametric situations. In this paper, we propose a new kernel-type regression estimator and study the limiting distribution of the properly normalized versions of the maximal deviation of the proposed estimator from the true regression curve. The resulting limiting distribution will be used to construct uniform confidence bands for the underlying regression curve with asymptotically correct coverages. The focus of the current paper is on the case where $$X\in \mathbb {R}$$. We also perform numerical studies to assess the finite-sample performance of the proposed method. In this paper, both mechanics and the theoretical validity of our methods are discussed.
Casein Kinase II Phosphorylation of Spt6 Enforces Transcriptional Fidelity by Maintaining Spn1-Spt6 Interaction.

Summary Spt6 is a histone chaperone that associates with RNA polymerase II and deposits nucleosomes in the wake of transcription. Although Spt6 has an essential function in nucleosome deposition, it is not known whether this function is influenced by post-translational modification. Here, we report that casein kinase II (CKII) phosphorylation of Spt6 is required for nucleosome occupancy at the 5′ ends of genes to prevent aberrant antisense transcription and enforce transcriptional directionality. Mechanistically, we show that CKII phosphorylation of Spt6 promotes the interaction of Spt6 with Spn1, a binding partner required for chromatin reassembly and full recruitment of Spt6 to genes. Our study defines a function for CKII phosphorylation in transcription and highlights the importance of post-translational modification in histone chaperone function.
Simple Approximations of the SIR Meta Distribution in General Cellular Networks

Compared to the standard success (coverage) probability, the meta distribution of the signal-to-interference ratio (SIR) provides much more fine-grained information about the network performance. We consider general heterogeneous cellular networks (HCNs) with base station tiers modeled by arbitrary stationary and ergodic non-Poisson point processes. The exact analysis of non-Poisson network models is notoriously difficult, even in terms of the standard success probability, let alone the meta distribution. Hence, we propose a simple approach to approximate the SIR meta distribution for non-Poisson networks based on the ASAPPP (“approximate SIR analysis based on the Poisson point process”) method. We prove that the asymptotic horizontal gap $G_{0}$ between its standard success probability and that for the Poisson point process exactly characterizes the gap between the $b$ th moment of the conditional success probability, as the SIR threshold goes to 0. The gap $G_{0}$ allows two simple approximations of the meta distribution for general HCNs: 1) the per-tier approximation by applying the shift $G_{0}$ to each tier and 2) the effective gain approximation by directly shifting the meta distribution for the homogeneous independent Poisson network. Given the generality of the model considered and the fine-grained nature of the meta distribution, these approximations work surprisingly well.
Population‐Based Study

A population-based study is a study of properties of a well-defined population, such as individuals residing in a defined geographic area in a given time period. Keywords: population-based study; population-based case–control study
Federalizing Benefits: The Introduction of Supplemental Security Income and the Size of the Safety Net

Abstract In 1974, Supplemental Security Income (SSI) federalized cash welfare programs for the elderly, blind, and individuals with disabilities, imposing a national minimum benefit, and differentially raising payment levels in states that paid below its benefit floor. We show that this increased disability participation, but shrank non-disability cash transfer programs. For every four new SSI recipients, three came from other welfare programs. Each dollar of per capita SSI income increased total per capita transfer income by just over 50 cents. Federalizing part of a patchwork safety net need not increase redistribution by as much as traditional models of fiscal federalism suggest.
Topological semimetal features in the multiferroic hexagonal manganites

Using first-principles calculations we examine the band structures of ferromagnetic hexagonal manganites ${\text{YXO}}_{3}$ ($\text{X}=\text{V}$, Cr, Mn, Fe, and Co) in the nonpolar nonsymmorphic $P{6}_{3}/mmc$ space group. For ${\mathrm{YVO}}_{3}$ and ${\mathrm{YCrO}}_{3}$ we find a band inversion near the Fermi energy that generates a nodal ring in the ${k}_{z}=0$ mirror plane. We perform a more detailed analysis for these compounds and predict the existence of the topological ``drumhead'' surface states. Finally, we briefly discuss the low-symmetry polar phases (space group $P{6}_{3}cm$) of these systems, and show they can undergo a $P{6}_{3}/mmc\ensuremath{\rightarrow}P{6}_{3}cm$ transition by condensation of soft ${K}_{3}$ and ${\mathrm{\ensuremath{\Gamma}}}_{2}^{\ensuremath{-}}$ phonons. Based on our findings, stabilizing these compounds in the hexagonal phase could offer a promising platform for studying the interplay of topology and multiferroicity, and the coexistence of real-space and reciprocal-space topological protection in the same phase.
Labeling food safety attributes: to inform or not to inform?

We examine the impact of food labels that make unsupported claims of food safety and labels that provide information to support such claims on consumer choices and examine consumers’ willingness to pay for beef products with these different food safety labeling cues. Empirical results from a survey of grocery shoppers in a Midwestern city in the USA show that more than two thirds of respondents who received a label with unsubstantiated food safety claims chose this option and were willing to pay the highest price premium for it, compared to the less preferred labeling options that provided information to support food safety claims.
Stereoselective synthesis of a tricyclic guanidinium model of cylindrospermopsin

Abstract An 11-step enantioselective synthesis of the A-ring of cylindrospermopsin is described using an intramolecular conjugate addition as the key step to forming the piperidine ring. Further elaboration generates a tricyclic guanidine via a sequential double displacement strategy as a model for the cylindrospermopsin guanidinium core.
The addition of SF5Br to acrylic esters: pathways to γ-SF5-propane derivatives

Abstract Reaction of SF 5 Br with various acrylic esters, CH 2 CHCOOR, where R = C 2 H 5 /C(CH 3 ) 3 and CH 2 C(CH 3 )COOCH 3 , CH 2 C(CH 2 Cl)-COOC 2 H 5 , CH 2 C( n -C 7 H 15 )COOC 2 H 5 resulted in 1:1 adducts with the SF 5 -group attached to the CH 2 portion of the acrylic ester. These adducts were converted to halogen-free saturated esters, an alcohol [SF 5 (CH 2 ) 3 OH] an acid [SF 5 (CH 2 ) 2 COOH] and a bromide [SF 5 (CH 2 ) 3 Br]. Therefore, a method for chain-elongation of SF 5 -aliphatic hydrocarbons has been devised. With ethyl acrylate, a 2:1 adduct was also isolated. The reaction of SF 5 Cl with (C 2 H 5 O) 2 CCHCOOC 2 H 5 gave chloromalonate. The compounds were characterized by their infrared, nuclear magnetic and mass spectral data.
Synthesis of aurones under neutral conditions using a deep eutectic solvent

Abstract Aurones are an interesting, but little studied member of the flavanoid family of natural products. Of the various methods available for their synthesis, the simplest involves the condensation of a coumaranone with an aldehyde. This reaction can be performed under acidic or basic conditions. We have recently discovered an effectively neutral set of conditions that employ the deep eutectic solvent comprised of choline chloride and urea as both solvent and catalyst. Modest to good yields can be achieved for a range of aldehydes, thereby facilitating further study of aurones from both a biological as well as a spectroscopic perspective.
Nucleophilic difluoromethylation of primary alkyl halides using difluoromethyl phenyl sulfone as a difluoromethyl anion equivalent.

A facile and efficient nucleophilic difluoromethylation of primary alkyl halides has been disclosed through a novel nucleophilic substitution−reductive desulfonylation strategy, using difluoromethyl phenyl sulfone as a difluoromethyl anion (“CF2H-”) equivalent.
Sixty years of the Grunwald-Winstein equation : development and recent applications

The development of scales of values for solvent nucleophilicity and for the aromatic-ring parameter are described. These are applied to solvolytic addition to carbocations and, together with improved solvent ionising power scales, to solvolyses proceeding with a 1,2-aryl shift and to solvolytic displacements at acyl carbon and at a heteroatom, such as phosphorus or sulfur.
A General Method for the Synthesis of the N2- and N6- Carcinogenic Amine Adducts of 2’-Deoxyguanosine and 2’Deoxyadenosine

A number of simple arylamino compounds (Figure 1) are well-established as pro-carcinogenic agents. Metabolic activation leads to a series of unstable N-hydroxy derivatives that on solvolysis, give nitrenium ions. The latter, which are regarded as the primary mutagenic/carcinogenic agents attack DNA to give a variety of adducts. Principal among these are the C-8 arylamination products of 2‘-deoxyguanosine (dG) and the N2- and N6-(2-acetylamino)arylation adducts of dG and 2‘-deoxyadenosine (dA), respectively. The latter types of adducts have received little biological attention because synthetic methods for their preparation have been lacking. We now describe a general high-yield method for the synthesis of both of these types of N-arylated 2‘-deoxynucleosides. The key step is a Buchwald−Hartwig coupling reaction between an appropriately protected derivative of dG or dA (1 and 7, respectively) and an o-nitroaryl bromide or triflate (2a−e). Subsequent reduction, acetylation, and deprotection of the N2-adduct...
Speciation in sympatry with ongoing secondary gene flow and a potential olfactory trigger in a radiation of Cameroon cichlids.

The process of sympatric speciation in nature remains a fundamental unsolved problem. Cameroon crater lake cichlid radiations were long regarded as one of the most compelling examples; however, recent work showed that their origins were more complex than a single colonization event followed by isolation. Here, we performed a detailed investigation of the speciation history of a radiation of Coptodon cichlids from Lake Ejagham, Cameroon, using whole-genome sequencing data. The existence of the Lake Ejagham Coptodon radiation is remarkable as this 0.5 km2 lake offers limited scope for divergence across a shallow depth gradient, disruptive selection is currently weak, and the species are sexually monochromatic. We infer that Lake Ejagham was colonized by Coptodon cichlids soon after its formation 9,000 years ago, yet speciation occurred only in the last 1,000-2,000 years. We show that secondary gene flow from a nearby riverine species has been ongoing, into ancestral as well as extant lineages, and we identify and date river-to-lake admixture blocks. One block contains a cluster of olfactory receptor genes that introgressed near the time of the first speciation event and coincides with a higher overall rate of admixture. Olfactory signalling is a key component of mate choice and species recognition in cichlids. A functional role for this introgression event is consistent with previous findings that sexual isolation appears much stronger than ecological isolation in Ejagham Coptodon. We conclude that speciation in this radiation took place in sympatry, yet may have benefited from ongoing riverine gene flow.
Product evolution in the Np(IV) fluorophosphate system

Abstract The reaction of 237NpO2 with Cs2CO3, Ga2O3, H3PO4, and HF under mild hydrothermal conditions leads to the formation of NpFPO4 after 4 days at 180 °C. Heating at 180 °C for an additional 6 days leads to the crystallization of Cs2Np2F7PO4 and NpF4. The Ga2O3 forms a GaPO4 matrix in which crystals of NpFPO4, Cs2Np2F7PO4, and NpF4 grow. Single crystal X-ray data reveal that the structure of NpFPO4 consists of Np(IV) centers bound by both fluoride and phosphate to yield [NpF2O6] distorted dodecahedra. These are linked by corner-sharing with fluoride and both corner- and edge-sharing with phosphate to yield a dense, three-dimensional network. The structure of Cs2Np2F7PO4 is complex and contains both distorted dodecahedral [NpO2F6] and tricapped trigonal prismatic [NpO2F7] environments around Np(IV) that are linked with each other through corner- and edge-sharing, and with the phosphate groups to create a three-dimensional structure. There are small channels extending down the a-axis in Cs2Np2F7PO4. Crystallographic data: NpFPO4, orthorhombic, space group Pnma, a=8.598(2), b=6.964(1), c=6.337(1) A, Z=4, V=379.44(13) A3, R(F)=3.53% for 40 parameters and 465 reflections with I>2σ(I) (T=193 K); Cs2Np2F7PO4, monoclinic, space group P21/c, a=8.8727(4), b=16.2778(7), c=7.8009(4) A, β=112.656(1), Z=4, V=1039.73(8) A3, R(F)=2.27% for 146 parameters and 2465 reflections with I>2σ(I) (T=193 K).