cssi/SciSciGPT-SciSciNet huggingface dataset:
When deep denoising meets iterative phase retrieval

Recovering a signal from its Fourier intensity underlies many important applications, including lensless imaging and imaging through scattering media. Conventional algorithms for retrieving the phase suffer when noise is present but display global convergence when given clean data. Neural networks have been used to improve algorithm robustness, but efforts to date are sensitive to initial conditions and give inconsistent performance. Here, we combine iterative methods from phase retrieval with image statistics from deep denoisers, via regularization-by-denoising. The resulting methods inherit the advantages of each approach and outperform other noise-robust phase retrieval algorithms. Our work paves the way for hybrid imaging methods that integrate machine-learned constraints in conventional algorithms.
Invisible decays of neutral hadrons

Invisible decays of neutral hadrons are evaluated as ordinary-mirror particle oscillations using the newly developed mirror matter model. Assuming equivalence of the $CP$ violation and mirror symmetry breaking scales for neutral kaon oscillations, rather precise values of the mirror matter model parameters are predicted for such ordinary-mirror particle oscillations. Not only do these parameter values satisfy the cosmological constraints, but they can also be used to precisely determine the oscillation or invisible decay rates of neutral hadrons. In particular, invisible decay branching fractions for relatively long-lived hadrons such as $K^0_L$, $K^0_S$, $\Lambda^0$, and $\Xi^0$ due to such oscillations are calculated to be $9.9\times 10^{-6}$, $1.8\times 10^{-6}$, $4.4\times 10^{-7}$, and $3.6\times 10^{-8}$, respectively. These significant invisible decays are readily detectable at existing accelerator facilities.
Anti-Selective Disclosure Regulation and Analyst Forecast Accuracy and Usefulness

Abstract We investigate regulations intended to stop managers from privately disclosing corporate information to analysts in a setting with enhanced potential to isolate regulatory effects: the European Union (EU) Market Abuse Directive (MAD), a common regulation implemented by member states with varying sanctions and enforcement resources. Following the implementation of MAD in a country, analyst forecasts become more accurate, with relatively little of the effect attributable to increased voluntary public disclosure by covered firms. The effect of MAD on analyst accuracy is stronger in countries with more stringent enforcement and sanction systems. Although the improvement in accuracy is associated with the implementation of MAD alone, stock prices do not respond more strongly to analyst forecast releases until after market-trading enforcement improves under subsequent EU legislation (MiFID).
Fenchel–Nielsen coordinates on the augmented moduli space of anti-de Sitter structures

In this paper we combine our recent work on regular globally hyperbolic maximal anti-de Sitter structures with the classical theory of globally hyperbolic maximal Cauchy-compact anti-de Sitter manifolds in order to define an augmented moduli space. Moreover, we introduce a coordinate system in this space that resembles the complex Fenchel–Nielsen coordinates for hyperbolic quasi-Fuchsian manifolds.
School’s Out: The Role of Summers in Understanding Achievement Disparities:

Summer learning loss (SLL) is a familiar and much-studied phenomenon, yet new concerns that measurement artifacts may have distorted canonical SLL findings create a need to revisit basic research o...
Mainly on the Plane: Deep Subsurface Bacterial Proteins Bind and Alter Clathrate Structure.

Gas clathrates are both a resource and a hindrance. They store massive quantities of natural gas but also can clog natural gas pipelines, with disastrous consequences. Eco-friendly technologies for...
Consumption of a Western-Style Diet Modulates the Response of the Murine Gut Microbiome to Ciprofloxacin

Dietary composition and antibiotic use have major impacts on the structure and function of the gut microbiome, often resulting in dysbiosis. Despite this, little research has been done to explore the role of host diet as a determinant of antibiotic-induced microbiome disruption. Here, we utilize a multi-omic approach to characterize the impact of Western-style diet consumption on ciprofloxacin-induced changes to gut microbiome structure and transcriptional activity. We found that Western diet consumption dramatically increased Bacteroides abundances and shifted the community toward the metabolism of simple sugars and mucus glycoproteins. Mice consuming a Western-style diet experienced a greater expansion of Firmicutes following ciprofloxacin treatment than those eating a control diet. Transcriptionally, we found that ciprofloxacin reduced the abundance of tricarboxylic acid (TCA) cycle transcripts on both diets, suggesting that carbon metabolism plays a key role in the response of the gut microbiome to this antibiotic. Despite this, we observed extensive diet-dependent differences in the impact of ciprofloxacin on microbiota function. In particular, at the whole-community level we detected an increase in starch degradation, glycolysis, and pyruvate fermentation following antibiotic treatment in mice on the Western diet, which we did not observe in mice on the control diet. Similarly, we observed diet-specific changes in the transcriptional activity of two important commensal bacteria, Akkermansia muciniphila and Bacteroides thetaiotaomicron, involving diverse cellular processes such as nutrient acquisition, stress responses, and capsular polysaccharide (CPS) biosynthesis. These findings demonstrate that host diet plays a role in determining the impacts of ciprofloxacin on microbiome composition and microbiome function.IMPORTANCE Due to the growing incidence of disorders related to antibiotic-induced dysbiosis, it is essential to determine how our "Western"-style diet impacts the response of the microbiome to antibiotics. While diet and antibiotics have profound impacts on gut microbiome composition, little work has been done to examine their combined effects. Previous work has shown that nutrient availability, influenced by diet, plays an important role in determining the extent of antibiotic-induced disruption to the gut microbiome. Thus, we hypothesize that the Western diet will shift microbiota metabolism toward simple sugar and mucus degradation and away from polysaccharide utilization. Because of bacterial metabolism's critical role in antibiotic susceptibility, this change in baseline metabolism will impact how the structure and function of the microbiome are impacted by ciprofloxacin exposure. Understanding how diet modulates antibiotic-induced microbiome disruption will allow for the development of dietary interventions that can alleviate many of the microbiome-dependent complications of antibiotic treatment.
A Class of Lower Bounds for Bayesian Risk with a Bregman Loss

A general class of Bayesian lower bounds when the underlying loss function is a Bregman divergence is demonstrated. This class can be considered as an extension of the Weinstein–Weiss family of bounds for the mean squared error and relies on finding a variational characterization of Bayesian risk. The approach allows for the derivation of a version of the Cramer–Rao bound that is specific to a given Bregman divergence. The effectiveness of the new bound is evaluated in the Poisson noise setting.
On the validity of the formal Edgeworth expansion for posterior densities

We consider a fundamental open problem in parametric Bayesian theory, namely the validity of the formal Edgeworth expansion of the posterior density. While the study of valid asymptotic expansions for posterior distributions constitutes a rich literature, the validity of the formal Edgeworth expansion has not been rigorously established. Several authors have claimed connections of various posterior expansions with the classical Edgeworth expansion, or have simply assumed its validity. Our main result settles this open problem. We also prove a lemma concerning the order of posterior cumulants which is of independent interest in Bayesian parametric theory. The most relevant literature is synthesized and compared to the newly-derived Edgeworth expansions. Numerical investigations illustrate that our expansion has the behavior expected of an Edgeworth expansion, and that it has better performance than the other existing expansion which was previously claimed to be of Edgeworth type.
Evidence-based methodology for obtaining commercial insurance coverage of stereotactic radiosurgery for intractable epilepsy.

Abstract Objectives The coverage policies of many commercial insurers in the United States do not include coverage of stereotactic radiosurgery (SRS) for intractable epilepsy despite recent Level I evidence supporting its efficacy. We sought to assess the efficacy of an evidence-based methodology in obtaining coverage approval of SRS for intractable epilepsy. Patients and Methods The clinical policy guidelines from five of the largest United States commercial insurers were reviewed for their language regarding coverage of SRS for epilepsy. An evidence-based questionnaire was created for temporal lobe epilepsy and extratemporal lobe epilepsy based on recent evidence. Telephone interviewers of Insurers assessed the likelihood of SRS coverage for an epilepsy patient meeting the clinical inclusion criteria in the questionnaire. This likelihood was assessed numerically based on interviewee response (2 = yes, 1 = dependent on peer-to-peer, 0 = no). Results Of the five policy guidelines, none included literature more recent than 2017. For TLE, 3/5 insurance companies indicated likely SRS coverage; 2/5 indicated peer-to-peer discussion dependence for patients meeting questionnaire criteria for a score of 8/10. For extratemporal TLE, 2/5 companies indicated likely SRS coverage and 3/5 indicated peer-to-peer discussion dependence for a total score of 7/10. Conclusion Creation of an evidence-based methodology in approaching commercial insurers greatly increased the likelihood of SRS coverage for an indication (intractable epilepsy) widely perceived as investigational. These results should pave the way for epilepsy patients to receive coverage should they be appropriate SRS candidates.
A Local Energy Estimate for Wave Equations on Metrics Asymptotically Close to Kerr

In this article, we prove a local energy estimate for the linear wave equation on metrics with slow decay to a Kerr metric with small angular momentum. As an application, we study the quasilinear wave equation $$\Box _{g(u, t, x)} u = 0$$ where the metric g(u, t, x) is close (and asymptotically equal) to a Kerr metric with small angular momentum g(0, t, x). Under suitable assumptions on the metric coefficients, and assuming that the initial data for u is small enough, we prove global existence and decay of the solution u.
Graph Neural Networks-based Clustering for Social Internet of Things

In this paper, we propose a machine learning process for clustering large-scale social Internet-of-things (SIoT) devices into several groups of related devices sharing strong relations. To this end, we generate undirected weighted graphs based on the historical dataset of IoT devices and their social relations. Using the adjacency matrices of these graphs and the IoT devices’ features, we embed the graphs’ nodes using a Graph Neural Network (GNN) to obtain numerical vector representations of the IoT devices. The vector representation does not only reflect the characteristics of the device but also its relations with its peers. The obtained node embeddings are then fed to a conventional unsupervised learning algorithm to determine the clusters accordingly. We showcase the obtained IoT groups using two well-known clustering algorithms, specifically the K-means and the density-based algorithm for discovering clusters (DBSCAN). Finally, we compare the performances of the proposed GNN-based clustering approach in terms of coverage and modularity to those of the deterministic Louvain community detection algorithm applied solely on the graphs created from the different relations. It is shown that the framework achieves promising preliminary results in clustering large-scale IoT systems.
An empirical examination of sample size effects on population demographic estimates in birds using single nucleotide polymorphism (SNP) data.

Sample size is a critical aspect of study design in population genomics research, yet few empirical studies have examined the impacts of small sample sizes. We used datasets from eight diverging bird lineages to make pairwise comparisons at different levels of taxonomic divergence (populations, subspecies, and species). Our data are from loci linked to ultraconserved elements and our analyses used one single nucleotide polymorphism per locus. All individuals were genotyped at all loci, effectively doubling sample size for coalescent analyses. We estimated population demographic parameters (effective population size, migration rate, and time since divergence) in a coalescent framework using Diffusion Approximation for Demographic Inference, an allele frequency spectrum method. Using divergence-with-gene-flow models optimized with full datasets, we subsampled at sequentially smaller sample sizes from full datasets of 6-8 diploid individuals per population (with both alleles called) down to 1:1, and then we compared estimates and their changes in accuracy. Accuracy was strongly affected by sample size, with considerable differences among estimated parameters and among lineages. Effective population size parameters (ν) tended to be underestimated at low sample sizes (fewer than three diploid individuals per population, or 6:6 haplotypes in coalescent terms). Migration (m) was fairly consistently estimated until <2 individuals per population, and no consistent trend of over-or underestimation was found in either time since divergence (T) or theta (Θ = 4Nrefμ). Lineages that were taxonomically recognized above the population level (subspecies and species pairs; that is, deeper divergences) tended to have lower variation in scaled root mean square error of parameter estimation at smaller sample sizes than population-level divergences, and many parameters were estimated accurately down to three diploid individuals per population. Shallower divergence levels (i.e., populations) often required at least five individuals per population for reliable demographic inferences using this approach. Although divergence levels might be unknown at the outset of study design, our results provide a framework for planning appropriate sampling and for interpreting results if smaller sample sizes must be used.
UPPER BOUNDS ON POLYNOMIALS WITH SMALL GALOIS GROUP

When monic integral polynomials of degree $n \geq 2$ are ordered by the maximum of the absolute value of their coefficients, the Hilbert irreducibility theorem implies that asymptotically 100% are irreducible and have Galois group isomorphic to $S_n$. In particular, amongst such polynomials whose coefficients are bounded by $B$ in absolute value, asymptotically $(1+o(1))(2B+1)^n$ are irreducible and have Galois group $S_n$. When $G$ is a proper transitive subgroup of $S_n$, however, the asymptotic count of polynomials with Galois group $G$ has been determined only in very few cases. Here, we show that if there are strong upper bounds on the number of degree $n$ fields with Galois group $G$, then there are also strong bounds on the number of polynomials with Galois group $G$. For example, for any prime $p$, we show that there are at most $O(B^{3 - \frac{2}{p}} (\log B)^{p - 1})$ polynomials with Galois group $C_p$ and coefficients bounded by $B$.
The Structure of Gaussian Minimal Bubbles

It is shown that m disjoint sets with fixed Gaussian volumes that partition $$\mathbb {R}^{n}$$ with minimum Gaussian surface area must be $$(m-1)$$ -dimensional. This follows from a second variation argument using infinitesimal translations. The special case $$m=3$$ proves the Double Bubble problem for the Gaussian measure, with an extra technical assumption. That is, when $$m=3$$ , the three minimal sets are adjacent 120 degree sectors. The technical assumption is that the triple junction points of the minimizing sets have polynomial volume growth. Assuming again the technical assumption, we prove the $$m=4$$ Triple Bubble Conjecture for the Gaussian measure. Our methods combine the Colding–Minicozzi theory of Gaussian minimal surfaces with some arguments used in the Hutchings–Morgan–Ritore-Ros proof of the Euclidean Double Bubble Conjecture.
Engineering of Phytosterol-Producing Yeast Platforms for Functional Reconstitution of Downstream Biosynthetic Pathways

As essential structural molecules for plant plasma membranes, phytosterols are key intermediates for the synthesis of many downstream specialized metabolites of pharmaceutical or agricultural significance, such as brassinosteroids and withanolides. Saccharomyces cerevisiae has been widely used as an alternative producer for plant secondary metabolites. Establishment of heterologous sterol pathways in yeast, however, has been challenging due to either low efficiency or structural diversity, likely a result of crosstalk between the heterologous phytosterol and the endogenous ergosterol biosynthesis. For example, in this study, we engineered campesterol production in yeast using plant enzymes; although we were able to enhance the titer of campesterol to ∼40 mg/L by upregulating the mevalonate pathway, no conversion to downstream products was detected upon the introduction of downstream plant enzymes. Further investigations uncovered two interesting observations about sterol engineering in yeast. First, many heterologous sterols tend to be efficiently and intensively esterified in yeast, which drastically impedes the function of downstream enzymes. Second, yeast can overcome the growth deficiency caused by altered sterol metabolism through repeated culture. By employing metabolic engineering, strain evolution, fermentation engineering, and pathway reconstitution, we were able to reconstruct the multienzyme pathways for the synthesis of a set of phytosterols: campesterol (∼7 mg/L), β-sitosterol (∼2 mg/L), 22-hydroxycampesterol (∼1 mg/L), and 22-hydroxycampest-4-en-3-one (∼4 mg/L). This work identified and addressed some of the technical bottlenecks in phytosterol-derived pathway reconstitution in the baker's yeast and opens up opportunities for efficient bioproduction and metabolic pathway elucidation of this group of phytochemicals.
Calretinin and calbindin architecture of the midline thalamus associated with prefrontal–hippocampal circuitry

The midline thalamus bidirectionally connects the medial prefrontal cortex (mPFC) and hippocampus (HC) creating a unique cortico-thalamo-cortical circuit fundamental to memory and executive function. While the anatomical connectivity of midline thalamus has been thoroughly investigated, little is known about its cellular organization within each nucleus. Here we used immunohistological techniques to examine cellular distributions in the midline thalamus based on the calcium binding proteins parvalbumin (PV), calretinin (CR), and calbindin (CB). We also examined these calcium binding proteins in a population of reuniens cells known to project to both mPFC and HC using a dual fluorescence retrograde adenoassociated virus-based tracing approach. These dual reuniens mPFC-HC projecting cells, in particular, are thought to be important for synchronizing mPFC and HC activity. First, we confirmed the absence of PV+ neurons in the midline thalamus. Second, we found a common pattern of CR+ and CB+ cells throughout midline thalamus with CR+ cells running along the nearby third ventricle (3V) and penetrating the midline. CB+ cells were consistently more lateral and toward the middle of the dorsal-ventral extent of the midline thalamus. Notably, single-labeled CR+ and CB+ zones were partially overlapping and included dual-labeled CR+ /CB+ cells. Within RE, we also observed a CR and CB subzone specific diversity. Interestingly, dual mPFC-HC projecting neurons in RE expressed none of the calcium binding proteins examined, but were contained in nests of CR+ and CB+ cells. Overall, the midline thalamus was well organized into CR+ and CB+ rich zones distributed throughout the region, with dual mPFC-HC projecting cells in reuniens representing a unique cell population. These results provide a cytoarchitectural organization in the midline thalamus based on calcium binding protein expression, and set the stage for future cell-type specific interrogations of the functional role of these different cell populations in mPFC-HC interactions.
Learning Invariant Feature Representation to Improve Generalization across Chest X-ray Datasets

Chest radiography is the most common medical image examination for screening and diagnosis in hospitals. Automatic interpretation of chest X-rays at the level of an entry-level radiologist can greatly benefit work prioritization and assist in analyzing a larger population. Subsequently, several datasets and deep learning-based solutions have been proposed to identify diseases based on chest X-ray images. However, these methods are shown to be vulnerable to shift in the source of data: a deep learning model performing well when tested on the same dataset as training data, starts to perform poorly when it is tested on a dataset from a different source. In this work, we address this challenge of generalization to a new source by forcing the network to learn a source-invariant representation. By employing an adversarial training strategy, we show that a network can be forced to learn a source-invariant representation. Through pneumonia-classification experiments on multi-source chest X-ray datasets, we show that this algorithm helps in improving classification accuracy on a new source of X-ray dataset.
Leveraging Cation Identity to Engineer Solid Electrolyte Interphases for Rechargeable Lithium Metal Anodes

Summary Engineering the solid electrolyte interphase (SEI) is a promising approach to improving Li metal battery performance. Recent work has shown that alkali metal additives can lead to smooth Li deposits, yet the underlying mechanisms are not understood. In this work, we demonstrate that alkali metal additives (here, K+) alter SEI composition, thickness, and solubility. Through post-mortem elemental analyses, we find that K+ ions do not deposit, but instead modify the reactivity of the electrode-electrolyte interface. Using quantitative nuclear magnetic resonance (NMR) and density functional theory (DFT), we show that K+ mitigates solvent decomposition at the Li metal surface. These findings suggest that alkali metal additives can be leveraged to suppress the formation of undesired SEI components (e.g., Li2CO3, soluble organic species), serving as an alternative approach for SEI modification compared to sacrificial additives. We believe that our work will spur further interest in the underexplored area of cation engineering.
Efficiently coupled microring circuit for on-chip cavity QED with trapped atoms

We present a complete fabrication study of an efficiently coupled microring optical circuit tailored for cavity quantum electrodynamics with trapped atoms. The microring structures are fabricated on a transparent membrane with high in-vacuum fiber edge-coupling efficiency in a broad frequency band. In addition, a bus waveguide pulley coupler realizes critical coupling to the microrings at both of the cesium D-line frequencies, while high coupling efficiency is achieved at the cesium “magic” wavelengths for creating a lattice of two-color evanescent field traps above a microring. The presented platform holds promise for realizing a robust atom-nanophotonics hybrid quantum device.
Accurate Detection of Wake Word Start and End Using a CNN.

Small footprint embedded devices require keyword spotters (KWS) with small model size and detection latency for enabling voice assistants. Such a keyword is often referred to as \textit{wake word} as it is used to wake up voice assistant enabled devices. Together with wake word detection, accurate estimation of wake word endpoints (start and end) is an important task of KWS. In this paper, we propose two new methods for detecting the endpoints of wake words in neural KWS that use single-stage word-level neural networks. Our results show that the new techniques give superior accuracy for detecting wake words' endpoints of up to 50 msec standard error versus human annotations, on par with the conventional Acoustic Model plus HMM forced alignment. To our knowledge, this is the first study of wake word endpoints detection methods for single-stage neural KWS.
A Joint Framework for Audio Tagging and Weakly Supervised Acoustic Event Detection Using DenseNet with Global Average Pooling

This paper proposes a network architecture mainly designed for audio tagging, which can also be used for weakly supervised acoustic event detection (AED). The proposed network consists of a modified DenseNet as the feature extractor, and a global average pooling (GAP) layer to predict frame-level labels at inference time. This architecture is inspired by the work proposed by Zhou et al., a well-known framework using GAP to localize visual objects given image-level labels. While most of the previous works on weakly supervised AED used recurrent layers with attention-based mechanism to localize acoustic events, the proposed network directly localizes events using the feature map extracted by DenseNet without any recurrent layers. In the audio tagging task of DCASE 2017, our method significantly outperforms the state-of-the-art method in F1 score by 5.3% on the dev set, and 6.0% on the eval set in terms of absolute values. For weakly supervised AED task in DCASE 2018, our model outperforms the state-of-the-art method in event-based F1 by 8.1% on the dev set, and 0.5% on the eval set in terms of absolute values, by using data augmentation and tri-training to leverage unlabeled data.
NC-smooth algebroid thickenings for families of vector bundles and quiver representations

In his work on deformation quantization of algebraic varieties Kontsevich introduced the notion of algebroid as a certain generalization of a sheaf of algebras. We construct algebroids which are given locally by NC-smooth thickenings in the sense of Kapranov, over two classes of smooth varieties: the bases of miniversal families of vector bundles on projective curves, and the bases of miniversal families of quiver representations.
Tensile Fracture of Welded Polymer Interfaces: Miscibility, Entanglements, and Crazing

Large-scale molecular simulations are performed to investigate tensile failure of polymer interfaces as a function of welding time t. Changes in the tensile stress, mode of failure and interfacial fracture energy GI are correlated to changes in the interfacial entanglements as determined from primitive path analysis. Bulk polymers fail through craze formation, followed by craze breakdown through chain scission. At small t welded interfaces are not strong enough to support craze formation and fail at small strains through chain pullout at the interface. Once chains have formed an average of about one entanglement across the interface, a stable craze is formed throughout the sample. The failure stress of the craze rises with welding time and the mode of craze breakdown changes from chain pullout to chain scission as the interface approaches bulk strength. The interfacial fracture energy GI is calculated by coupling the simulation results to a continuum fracture mechanics model. As in experiment, GI increase...
Random Close Packing of Granular Matter

We propose an interpretation of the random close packing of granular materials as a phase transition, and discuss the possibility of experimental verification.
Lempel-Ziv Jaccard Distance, an effective alternative to ssdeep and sdhash

Abstract Recent work has proposed the Lempel-Ziv Jaccard Distance (LZJD) as a method to measure the similarity between binary byte sequences for malware classification. We propose and test LZJD's effectiveness as a similarity digest hash for digital forensics. To do so we develop a high performance Java implementation with the same command-line arguments as sdhash, making it easy to integrate into existing work-flows. Our testing shows that LZJD is effective for this task, and significantly outperforms sdhash and ssdeep in its ability to match related file fragments and files corrupted with random noise. In addition, LZJD is up to 60× faster than sdhash at comparison time.
Anomalous dimensions of non-chiral operators from AdS/CFT

Non-chiral operators with positive anomalous dimensions can have interesting applications to supersymmetric model building. Motivated by this, we develop a new method for obtaining the anomalous dimensions of non-chiral double-trace operators in \( \mathcal{N} = 1 \) superconformal field theories (SCFTs) with weakly-coupled AdS duals. Via the Hamiltonian formulation ofAdS/CFT, we show how to directly compute the anomalous dimension as a bound state energy in the gravity dual. This simplifies previous approaches based on the four-point function and the OPE. We apply our method to a class of effective AdS5 supergravity models, and we find that the binding energy can have either sign. If such models can be UV completed, they will provide the first calculable examples of SCFTs with positive anomalous dimensions.
Quasi-regular sequences and optimal schedules for security games

We study security games in which a defender commits to a mixed strategy for protecting a finite set of targets of different values. An attacker, knowing the defender's strategy, chooses which target to attack and for how long. If the attacker spends time t at a target i of value αi, and if he leaves before the defender visits the target, his utility is t · αi; if the defender visits before he leaves, his utility is 0. The defender's goal is to minimize the attacker's utility. The defender's strategy consists of a schedule for visiting the targets; it takes her unit time to switch between targets. Such games are a simplified model of a number of real-world scenarios such as protecting computer networks from intruders, crops from thieves, etc. We show that optimal defender play for such security games, although played in continuous time, reduces to the solution of a combinatorial question regarding the existence of infinite sequences over a finite alphabet, with the following properties for each symbol i: (1) i constitutes a prescribed limiting fraction pi of the sequence. (2) The occurrences of i are spread apart close to evenly, in that the ratio of the longest to shortest interval between consecutive occurrences is bounded by a parameter K. We call such sequences K-quasi-regular; a 1-quasi-regular sequence is one in which the occurrences of each symbol form an arithmetic sequence. As we show, a 1-quasi-regular sequence ensures an optimal defender strategy for these security games: the intuition for this fact lies in the famous "inspection paradox." However, as we demonstrate, for K pi, K-quasi-regular sequences may not exist. Fortunately, this does not turn out to be an obstruction: we show that, surprisingly, 2-quasi-regular sequences also suffice for optimal defender play. What is more, even randomized 2-quasi-regular sequences suffice for optimality. We show that such sequences always exist, and can be calculated efficiently. Thus, we can ensure optimal defender play for these security games. The question of the least K for which deterministic K-quasi-regular sequences exist is fascinating. Using an ergodic theoretical approach, we proceed to show that deterministic 3-quasi-regular sequences always exist (and can be calculated efficiently). We also show that these deterministic 3-regular sequences give rise to a a 1.006-approximation algorithm for the defender's optimal strategy. For 2 ≤ K K-quasi-regular sequences always exist; however, when the pi are all small, improved bounds are possible, and in fact, (1 + ∈)-quasi-regular deterministic sequences exist for any ∈ > 0 for sufficiently small pi.
Strain-induced energy band gap opening in two-dimensional bilayered silicon film

This work presents a theoretical study of the structural and electronic properties of bilayered silicon film (BiSF) under in-plane biaxial strain/stress using density functional theory (DFT). Atomic structures of the two-dimensional (2-D) silicon films are optimized by using both the local-density approximation (LDA) and generalized gradient approximation (GGA). In the absence of strain/stress, five buckled hexagonal honeycomb structures of the BiSF with triangular lattice have been obtained as local energy minima, and their structural stability has been verified. These structures present a Dirac-cone shaped energy band diagram with zero energy band gaps. Applying a tensile biaxial strain leads to a reduction of the buckling height. Atomically flat structures with zero buckling height have been observed when the AA-stacking structures are under a critical biaxial strain. Increase of the strain between 10.7% and 15.4% results in a band-gap opening with a maximum energy band gap opening of ∼0.17 eV, obtained when a 14.3% strain is applied. Energy band diagrams, electron transmission efficiency, and the charge transport property are calculated. Additionally, an asymmetric energetically favorable atomic structure of BiSF shows a non-zero band gap in the absence of strain/stress and a maximum band gap of 0.15 eV as a −1.71% compressive strain is applied. Both tensile and compressive strain/stress can lead to a band gap opening in the asymmetric structure.
Phase Space Homogenization of Noisy Hamiltonian Systems

We study the dynamics of an inertial particle coupled to forcing, dissipation, and noise in the small mass limit. We derive an expression for the limiting (homogenized) joint distribution of the position and (scaled) velocity degrees of freedom. In particular, weak convergence of the joint distributions is established, along with a bound on the convergence rate for a wide class of expected values.
Linear Growth of Spiral SASI Modes in Core‐Collapse Supernovae

Two-dimensional axisymmetric simulations have shown that the postbounce accretion shock in core-collapse supernovae is subject to the spherical accretion shock instability, or SASI. Recent three-dimensional simulations have revealed the existence of a nonaxisymmetric mode of the SASI as well, where the postshock flow displays a spiral pattern. Here we investigate the growth of these spiral modes using two-dimensional simulations of the postbounce accretion flow in the equatorial plane of a core-collapse supernova. By perturbing a steady state model we are able to excite both one-, two-, and three-armed spiral modes that grow exponentially with time, demonstrating that these are linearly unstable modes closely related to the original axisymmetric sloshing modes. By tracking the distribution of angular momentum, we show that these modes are able to efficiently separate the angular momentum of the accretion flow (which maintains a net angular momentum of zero), leading to a significant spin-up of the underlying accreting proto-neutron star.
Grand unified theory with a stable proton

Author(s): Fornal, Bartosz; Grinstein, Benjamin | Abstract: We demonstrate that a phenomenologically viable four-dimensional grand unified theory with no proton decay can be constructed. This is done in the framework of the minimal nonsupersymmetric SU(5) GUT by introducing new representations and separating the physical quark and lepton fields into different multiplets. In such a theory all beyond Standard Model particles are naturally heavy, but one can tune the parameters of the model such that gauge coupling unification is achieved and some of the new particles are at the TeV scale and accessible at the LHC.
Uncovering Ramanujan’s “Lost” Notebook: an oral history

Here we weave together interviews conducted by the author with three prominent figures in the world of Ramanujan’s mathematics, George Andrews, Bruce Berndt and Ken Ono. The article describes Andrews’s discovery of the “lost” notebook, Andrews and Berndt’s effort of proving and editing Ramanujan’s notes, and recent breakthroughs by Ono and others carrying certain important aspects of the Indian mathematician’s work into the future. Also presented are historical details related to Ramanujan and his mathematics, perspectives on the impact of his work in contemporary mathematics, and a number of interesting personal anecdotes from Andrews, Berndt and Ono.
Interstellar Gas-phase Element Depletions in the Small Magellanic Cloud: A Guide to Correcting for Dust in QSO Absorption Line Systems*

We present data on the gas phase abundances for 9 different elements in the interstellar medium of the Small Magellanic Cloud (SMC), based on the strengths of ultraviolet absorption features over relevant velocities in the spectra of 18 stars within the SMC. From this information and the total abundances defined by the element fractions in young stars in the SMC, we construct a general interpretation on how these elements condense into solid form onto dust grains. As a group, the elements Si, S, Cr, Fe, Ni, and Zn exhibit depletion sequences similar to those in the local part of our Galaxy defined by Jenkins (2009). The elements Mg and Ti deplete less rapidly in the SMC than in the Milky Way, and Mn depletes more rapidly. We speculate that these differences might be explained by the different chemical affinities to different existing grain substrates. For instance, there is evidence that the mass fractions of polycyclic aromatic hydrocarbons (PAHs) in the SMC are significantly lower than those in the Milky Way. We propose that the depletion sequences that we observed for the SMC may provide a better model for interpreting the element abundances in low metallicity Damped Lyman Alpha (DLA) and sub-DLA absorption systems that are recorded in the spectra of distant quasars and gamma ray burst afterglows.
Thermal Equilibrium Curves and Turbulent Mixing in Keplerian Accretion Disks

We consider vertical heat transport in Keplerian accretion disks, including the effects of radiation, convection, and turbulent mixing driven by the Balbus-Hawley instability, in astronomical systems ranging from dwarf novae (DNe) and soft X-ray transients (SXTs) to active galactic nuclei (AGNs). In order to account for the interplay between convective and turbulent energy transport in a shearing environment, we propose a modified, anisotropic form of mixing-length theory that includes radiative and turbulent damping. We also include turbulent heat transport, which acts everywhere within disks, regardless of whether or not they are stably stratified and can move entropy in either direction. We have generated a series of vertical structure models and thermal equilibrium curves using the scaling law for the viscosity parameter α suggested by the exponential decay of the X-ray luminosity in SXTs. We have also included equilibrium curves for DNe using an α that is constant down to a small magnetic Reynolds number (~104). Our models indicate that weak convection is usually eliminated by turbulent radial mixing, even when mixing length estimates of convective vertical heat transport are much larger than turbulent heat transport. The substitution of turbulent heat transport for convection is more important on the unstable branches of thermal equilibrium S curves when α is larger. The low temperature turnover points Σmax on the equilibrium S curves are significantly reduced by turbulent mixing in DN and SXT disks. However, in AGN disks the standard mixing-length theory for convection is still a useful approximation when we use the scaling law for α since these disks are very thin at the relevant radii. In accordance with previous work, we find that constant α models give almost vertical S curves in the Σ-T plane and consequently imply very slow, possibly oscillating, cooling waves.
EFFICIENT VARIABLE-COEFFICIENT FINITE-VOLUME STOKES SOLVERS

We investigate several robust preconditioners for solving the saddle-point linear systems that arise from spatial discretization of unsteady and steady variable-coefficient Stokes equations on a uniform staggered grid. Building on the success of using the classical projection method as a preconditioner for the coupled velocity pressure system [B. E. Griffith, J. Comp. Phys., 228 (2009), pp. 7565-7595], as well; established techniques for steady and unsteady Stokes flow in the finite-element literature, we construct preconditioners that employ independent generalized Helmholtz and Poisson solvers for the velocity and pressure subproblems. We demonstrate that only a single cycle of a standard geometric multigrid algorithm serves as an effective inexact solver for each of these subproblems. Contrary to traditional wisdom, we find that the Stokes problem can be solved nearly as efficiently as the independent pressure and velocity subproblems, making the overall cost of solving the Stokes system comparable to the cost of classical projection or fractional step methods for incompressible flow, even for steady flow and in the presence of large density and viscosity contrasts. Two of the five preconditioners considered here are found to be robust to GMRES restarts and to increasing problem size, making them suitable for large-scale problems. Our work opens many possibilities for constructing novel unsplit temporal integrators for finite-volume spatial discretizations of the equations of low Mach and incompressible flow dynamics.
SPECTRUM OF SOFT MODE WITH THERMAL MASS OF QUARKS ABOVE CRITICAL TEMPERATURE

We study effects of a thermal-quark mass and Landau damping of quarks on the chiral phase transition and its soft modes at finite temperature. For this purpose we employ a simple model with the quark propagator obtained in the hard thermal loop approximation. We show that the chiral phase transition is second order even if quark have a finite thermal mass, and the thermal mass suppresses the chiral condensate. We argue that mesonic spectra have a large width due to scattering between gluons and quarks, and the van Hove singularity at threshold.1.
Subleading soft dressings of asymptotic states in QED and perturbative quantum gravity

We construct Faddeev-Kulish states in QED and perturbative quantum gravity to subleading order in the soft momentum expansion and to first order in the coupling constant, using the charge conservation formula of asymptotic symmetries associated with the tree-level subleading soft theorems. We demonstrate that the emission and absorption of soft photons/gravitons in dressed amplitudes vanish. The fact that no additional soft radiation may be added to a dressed amplitude supports the claim that, in the dressed state formalism, the soft and hard sectors of scattering processes are correlated. We also show that the dressed virtual amplitudes are equivalent to the infrared-finite part of the traditional amplitudes constructed using Fock states. Since there is no real soft radiation in the asymptotic Hilbert space, the dressed state formalism gives the same cross sections as the Bloch-Nordsieck method.
Axion protection from flavor

The QCD axion fails to solve the strong CP problem unless all explicit PQ violating, Planck-suppressed, dimension n < 10 operators are forbidden or have exponentially small coefficients. We show that all theories with a QCD axion contain an irreducible source of explicit PQ violation which is proportional to the determinant of the Yukawa interaction matrix of colored fermions. Generically, this contribution is of low operator dimension and will drastically destabilize the axion potential, so its suppression is a necessary condition for solving the strong CP problem. We propose a mechanism whereby the PQ symmetry is kept exact up to n = 12 with the help of the very same flavor symmetries which generate the hierarchical quark masses and mixings of the SM. This “axion flavor protection” is straightforwardly realized in theories which employ radiative fermion mass generation and grand unification. A universal feature of this construction is that the heavy quark Yukawa couplings are generated at the PQ breaking scale.
Distributional Modes for Scalar Field Quantization

We propose a mode-sum formalism for the quantization of the scalar field based on distributional modes, which are naturally associated with a slight modification of the standard plane-wave modes. We show that this formalism leads to the standard Rindler temperature result, and that these modes can be canonically defined on any Cauchy surface.
THE HISTORY OF THE GURALNIK, HAGEN AND KIBBLE DEVELOPMENT OF THE THEORY OF SPONTANEOUS SYMMETRY BREAKING AND GAUGE PARTICLES

I discuss historical material about the beginning of the ideas of spontaneous symmetry breaking and particularly the role of the paper by Guralnik, Hagen and Kibble in this development. I do so adding a touch of some more modern ideas about the extended solution-space of quantum field theory resulting from the intrinsic nonlinearity of nontrivial interactions.
From Dyck Paths to Standard Young Tableaux

We present nine bijections between classes of Dyck paths and classes of standard Young tableaux (SYT). In particular, we consider SYT of flag and rectangular shapes, we give Dyck path descriptions for certain SYT of height at most 3, and we introduce a special class of labeled Dyck paths of semilength n that is shown to be in bijection with the set of all SYT with n boxes. In addition, we present bijections from certain classes of Motzkin paths to SYT. As a natural framework for some of our bijections, we introduce a class of set partitions which in some sense is dual to the known class of noncrossing partitions.
High precision tuning of state for memristive devices by adaptable variation-tolerant algorithm

Using memristive properties common for titanium dioxide thin film devices, we designed a simple write algorithm to tune device conductance at a specific bias point to 1% relative accuracy (which is roughly equivalent to seven-bit precision) within its dynamic range even in the presence of large variations in switching behavior. The high precision state is nonvolatile and the results are likely to be sustained for nanoscale memristive devices because of the inherent filamentary nature of the resistive switching. The proposed functionality of memristive devices is especially attractive for analog computing with low precision data. As one representative example we demonstrate hybrid circuitry consisting of an integrated circuit summing amplifier and two memristive devices to perform the analog multiply-and-add (dot-product) computation, which is a typical bottleneck operation in information processing.
Parallel vectorized algebraic AES in Matlab for rapid prototyping of encrypted sensor processing algorithms and database analytics

The increasing use of networked sensor systems and networked databases has led to an increased interest in incorporating encryption directly into sensor algorithms and database analytics. Matlab is the dominant tool for rapid prototyping of sensor algorithms and has extensive database analytics capabilities. The advent of high level and high performance Galois Field mathematical environments allows encryption algorithms to be expressed succinctly and efficiently. This work leverages the Galois Field primitives found the Matlab Communication Toolbox to implement a mode of the Advanced Encrypted Standard (AES) based on first principals mathematics. The resulting implementation requires 100x less code than standard AES implementations and delivers speed that is effective for many design purposes. The parallel version achieves speed comparable to native OpenSSL on a single node and is sufficient for real-time prototyping of many sensor processing algorithms and database analytics.