cssi/SciSciGPT-SciSciNet huggingface dataset:
Following the Ground States of Full‐RSB Spherical Spin Glasses

We focus on spherical spin glasses whose Parisi distribution has support of the form $[0,q]$. For such models we construct paths from the origin to the sphere which consistently remain close to the ground-state energy on the sphere of corresponding radius. The construction uses a greedy strategy, which always follows a direction corresponding to the most negative eigenvalues of the Hessian of the Hamiltonian. For finite mixtures $\nu(x)$ it provides an algorithm of time complexity $O(N^{{\rm deg}(\nu)})$ to find w.h.p. points with the ground-state energy, up to a small error. For the pure spherical models, the same algorithm reaches the energy $-E_{\infty}$, the conjectural terminal energy for gradient descent. Using the TAP formula for the free energy, for full-RSB models with support $[0,q]$, we are able to prove the correct lower bound on the free energy (namely, prove the lower bound from Parisi's formula), assuming the correctness of the Parisi formula only in the replica symmetric case.
Escaping the Densification Plateau in Cellular Networks Through mmWave Beamforming

We study how dense multi-antenna millimeter wave (mmWave) cellular network performance scales in terms of the base station (BS) spatial density $\lambda $ , by studying the signal-to-interference-plus-noise ratio (SINR) and the area spectral efficiency (ASE). If the number of antennas at each BS scales at least linearly with $\lambda $ , which increases the number of possible beam configurations and their main-lobe gain, and decreases their side-lobe gain, we prove that the SINR approaches a finite random variable that is independent of $\lambda $ and the ASE scales at least linearly with $\lambda $ . In contrast, if the number of antennas scales sub-linearly with $\lambda $ , then the SINR decays to zero and the ASE saturates to a constant. Thus, by moving to higher carrier frequencies with successively smaller antennas, and exploiting the correspondingly increased directionality, cellular operators can in principle avoid the densification plateau (or collapse) in cellular networks and instead continue to harvest linear sum throughput gains through BS densification.
Conformal Graph Directed Markov Systems on Carnot Groups

We develop a comprehensive theory of conformal graph directed Markov systems in the non-Riemannian setting of Carnot groups equipped with a sub-Riemannian metric. In particular, we develop the thermodynamic formalism and show that, under natural hypotheses, the limit set of an Carnot conformal GDMS has Hausdorff dimension given by Bowen's parameter. We illustrate our results for a variety of examples of both linear and nonlinear iterated function systems and graph directed Markov systems in such sub-Riemannian spaces. These include the Heisenberg continued fractions introduced by Lukyanenko and Vandehey as well as Kleinian and Schottky groups associated to the non-real classical rank one hyperbolic spaces.
Response of Vertical Velocities in Extratropical Precipitation Extremes to Climate Change

Precipitation extremes intensify in most regions in climate-model projections. Changes in vertical velocities contribute to the changes in intensity of precipitation extremes but remain poorly understood. Here, we find that mid-tropospheric vertical velocities in extratropical precipitation extremes strengthen overall in simulations of 21st-century climate change. For each extreme event, we solve the quasi-geostrophic omega equation to decompose this strengthening into different physical contributions. We first consider a dry decomposition in which latent heating is treated as an external forcing of upward motion. Much of the positive contribution to upward motion from increased latent heating is offset by negative contributions from increases in dry static stability and changes in the horizontal length scale of vertical velocities. However, taking changes in latent heating as given is a limitation when the aim is to understand changes in precipitation, since latent heating and precipitation are closely linked. Therefore, we also perform a moist decomposition of the changes in vertical velocities in which latent heating is represented through a moist static stability. In the moist decomposition, changes in moist static stability play a key role and contributions from other factors such as changes in the depth of the upward motion increase in importance. While both dry and moist decompositions are self-consistent, the moist dynamical perspective has greater potential to give insights into the causes of the dynamical contributions to changes in precipitation extremes in different regions.
Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey

Latest technological improvements increased the quality of transportation. New data-driven approaches bring out a new research direction for all control-based systems, e.g., in transportation, robotics, IoT and power systems. Combining data-driven applications with transportation systems plays a key role in recent transportation applications. In this paper, the latest deep reinforcement learning (RL) based traffic control applications are surveyed. Specifically, traffic signal control (TSC) applications based on (deep) RL, which have been studied extensively in the literature, are discussed in detail. Different problem formulations, RL parameters, and simulation environments for TSC are discussed comprehensively. In the literature, there are also several autonomous driving applications studied with deep RL models. Our survey extensively summarizes existing works in this field by categorizing them with respect to application types, control models and studied algorithms. In the end, we discuss the challenges and open questions regarding deep RL-based transportation applications.
GeoFusion: Geometric Consistency informed Scene Estimation in Dense Clutter

We propose GeoFusion, a SLAM-based scene estimation method for building an object-level semantic map in dense clutter. In dense clutter, objects are often in close contact and severe occlusions, which brings more false detections and noisy pose estimates from existing perception methods. To solve these problems, our key insight is to consider geometric consistency at the object level within a general SLAM framework. The geometric consistency is defined in two parts: geometric consistency score and geometric relation. The geometric consistency score describes the compatibility between object geometry model and observation point cloud. Meanwhile, it provides a reliable measure to filter out false positives in data association. The geometric relation represents the relationship (e.g., contact) between geometric features (e.g., planes) among objects. The geometric relation makes the graph optimization for poses more robust and accurate. GeoFusion can robustly and efficiently infer the object labels, 6D object poses, and spatial relations from continuous noisy semantic measurements. We quantitatively evaluate our method using observations from a Fetch mobile manipulation robot. Our results demonstrate greater robustness against false estimates than frame-by-frame pose estimation from the state-of-the-art convolutional neural network.
Phase II Trial of Palbociclib in Patients with Advanced Esophageal or Gastric Cancer.

Lessons learned Palbociclib monotherapy demonstrated minimal clinical activity in patients with previously treated gastroesophageal cancers. Further clinical evaluation of palbociclib monotherapy is not warranted in gastroesophageal cancers, but improved understanding of resistance mechanisms may permit rational combination approaches. Background Dysregulation of the cell cycle is a hallmark of cancer. Progression through the G1/S transition requires phosphorylation of retinoblastoma (RB) by cyclin-dependent kinases (CDKs) 4 and 6, which are regulated by cyclins D and E. Amplifications of cyclin D loci and activating mutations in CDKs are frequent molecular aberrations in gastroesophageal malignancies. We conducted a phase II trial of the CDK4/6 inhibitor palbociclib as an initial test of efficacy. Methods Patients with previously treated metastatic gastroesophageal cancers with intact RB nuclear expression by immunohistochemistry were treated with 125 mg daily of palbociclib for days 1-21 of 28-day cycles. The primary endpoint was overall response rate. Results We screened 29 patients and enrolled 21 patients: 5 with gastric adenocarcinoma, 3 with gastroesophageal junction adenocarcinoma, 8 with esophageal adenocarcinoma, and 5 with esophageal squamous cell carcinoma. All 29 tumors screened had intact nuclear RB expression, and four treated patients tested positive for CCND1 overexpression. No objective responses were seen. Median progression-free survival was 1.8 months, and median overall survival was 3.0 months. All recurrent grade 3 or 4 toxicities were hematologic, with neutropenia in eight patients (38%), anemia in four patients (19%), and thrombocytopenia in two patients (10%). Conclusion Palbociclib has limited single-agent activity in gastroesophageal tumors.
Searching a database of source codes using contextualized code search

Consider the case where a programmer has written some part of a program, but has left part of the program (such as a method or a function body) incomplete. The goal is to use the context surrounding the missing code to automatically 'figure out' which of the codes in the database would be useful to the programmer in order to help complete the missing code. The search is 'contextualized' in the sense that the search engine should use clues in the partially-completed code to figure out which database code is most useful. The user should not be required to formulate an explicit query. We cast contextualized code search as a learning problem, where the goal is to learn a distribution function computing the likelihood that each database code completes the program, and propose a neural model for predicting which database code is likely to be most useful. Because it will be prohibitively expensive to apply a neural model to each code in a database of millions or billions of codes at search time, one of our key technical concerns is ensuring a speedy search. We address this by learning a 'reverse encoder' that can be used to reduce the problem of evaluating each database code to computing a convolution of two normal distributions.
Integrating Deep Learning in Domain Sciences at Exascale

This paper presents some of the current challenges in designing deep learning artificial intelligence (AI) and integrating it with traditional high-performance computing (HPC) simulations. We evaluate existing packages for their ability to run deep learning models and applications on large-scale HPC systems efficiently, identify challenges, and propose new asynchronous parallelization and optimization techniques for current large-scale heterogeneous systems and upcoming exascale systems. These developments, along with existing HPC AI software capabilities, have been integrated into MagmaDNN, an open-source HPC deep learning framework. Many deep learning frameworks are targeted at data scientists and fall short in providing quality integration into existing HPC workflows. This paper discusses the necessities of an HPC deep learning framework and how those needs can be provided (e.g., as in MagmaDNN) through a deep integration with existing HPC libraries, such as MAGMA and its modular memory management, MPI, CuBLAS, CuDNN, MKL, and HIP. Advancements are also illustrated through the use of algorithmic enhancements in reduced- and mixed-precision, as well as asynchronous optimization methods. Finally, we present illustrations and potential solutions for enhancing traditional compute- and data-intensive applications at ORNL and UTK with AI. The approaches and future challenges are illustrated in materials science, imaging, and climate applications.
More About Areas and Centers of Poncelet Polygons

We study the locus of the Circumcenter of Mass of Poncelet polygons, and the limit of the Center of Mass (when we consider the polygon as a “homogeneous lamina”) for degenerate Poncelet polygons. We also provide a proof for one of Dan Reznik invariants for billiard trajectories. Plus, we take a look at how the scene looks like when we shift to spherical geometry.
Machine learning materials physics: Multi-resolution neural networks learn the free energy and nonlinear elastic response of evolving microstructures

Abstract Many important multi-component crystalline solids undergo mechanochemical spinodal decomposition: a phase transformation in which the compositional redistribution is coupled with structural changes of the crystal, resulting in dynamically evolving microstructures. The ability to rapidly compute the macroscopic behavior based on these detailed microstructures is of paramount importance for accelerating material discovery and design. Here, our focus is on the macroscopic, nonlinear elastic response of materials harboring microstructure. Because of the diversity of microstructural patterns that can form, there is interest in taking a purely computational approach to predicting their macroscopic response. However, the evaluation of macroscopic, nonlinear elastic properties purely based on direct numerical simulations (DNS) is computationally very expensive, and hence impractical for material design when a large number of microstructures need to be tested. A further complexity of a hierarchical nature arises if the elastic free energy and its variation with strain is a small-scale fluctuation on the dominant trajectory of the total free energy driven by microstructural dynamics. To address these challenges, we present a data-driven approach, which combines advanced neural network (NN) models with DNS to predict the homogenized, macroscopic, mechanical free energy and stress fields arising in a family of multi-component crystalline solids that develop microstructure. The microstructures are numerically generated by solving a coupled, mechanochemical spinodal decomposition problem governed by nonlinear strain gradient elasticity and the Cahn–Hilliard phase field equation. The hierarchical structure of the free energy’s evolution induces a multi-resolution character to the machine learning paradigm: We construct knowledge-based neural networks (KBNNs) with either pre-trained fully connected deep neural networks (DNNs), or pre-trained convolutional neural networks (CNNs) that describe the dominant characteristic of the data to fully represent the hierarchically evolving free energy. We demonstrate multi-resolution learning of the materials physics; specifically of the nonlinear elastic response for both fixed and evolving microstructures.
Third Millennium Smart Cyberspace

The Third Millennium is a new era in the Smart Fully Automated Cyberspace that is becoming pervasive in its nature while connecting the next generation of Ultra-Smart Robotic Devices with the computationally powerful Smart Computational Devices (SCDs) accessible to anyone, anywhere and at any time. In support of Smart World, the telecommunications networks providers and SCDs developers, are working together to create much faster transmission channels with provision of higher quality of service for any multimedia content for anyone, anywhere at any time.The Human Machine Interface with high definition audio and video facilitates seamless control of Smart Robotics
DeepHammer: Depleting the Intelligence of Deep Neural Networks through Targeted Chain of Bit Flips

Security of machine learning is increasingly becoming a major concern due to the ubiquitous deployment of deep learning in many security-sensitive domains. Many prior studies have shown external attacks such as adversarial examples that tamper with the integrity of DNNs using maliciously crafted inputs. However, the security implication of internal threats (i.e., hardware vulnerability) to DNN models has not yet been well understood. In this paper, we demonstrate the first hardware-based attack on quantized deep neural networks-DeepHammer-that deterministically induces bit flips in model weights to compromise DNN inference by exploiting the rowhammer vulnerability. DeepHammer performs aggressive bit search in the DNN model to identify the most vulnerable weight bits that are flippable under system constraints. To trigger deterministic bit flips across multiple pages within reasonable amount of time, we develop novel system-level techniques that enable fast deployment of victim pages, memory-efficient rowhammering and precise flipping of targeted bits. DeepHammer can deliberately degrade the inference accuracy of the victim DNN system to a level that is only as good as random guess, thus completely depleting the intelligence of targeted DNN systems. We systematically demonstrate our attacks on real systems against 12 DNN architectures with 4 different datasets and different application domains. Our evaluation shows that DeepHammer is able to successfully tamper DNN inference behavior at run-time within a few minutes. We further discuss several mitigation techniques from both algorithm and system levels to protect DNNs against such attacks. Our work highlights the need to incorporate security mechanisms in future deep learning system to enhance the robustness of DNN against hardware-based deterministic fault injections.
Facilitating Access to Cross-Border Supplies of Patented Pharmaceuticals: The Case of the COVID-19 Pandemic

The COVID-19 pandemic has brought into stark relief the gaps in global preparedness to address widespread outbreaks of deadly viral infections. This article proposes legal mechanisms for addressing critical issues facing the international community in terms of providing equitable access to vaccines, treatments, diagnostics and medical equipment. On the supply side, the authors propose the establishment of mandatory patent pools (‘Licensing Facilities’) on a global or regional, or even national basis, depending upon the degree of cooperation that may be achieved. The authors also discuss the importance of creating shared production facilities. On the demand side, the authors propose the establishment of Regional Pharmaceutical Supply Centers for the collective procurement of products, and the need to coordinate the issuance of necessary compulsory licenses for production and/or importation, depending on relevant circumstances. The authors envisage that centralized coordination by Regional Pharmaceutical Supply Centers should assist in overcoming difficulties individual countries may encounter in addressing administrative and technical issues in procuring supplies, as well as creating improved bargaining leverage with potential suppliers. The authors finally address the problem created by the decision of various High-Income Countries (HICs) to ‘opt out’ as eligible importing countries under the WTO TRIPS Agreement Article 31bis amendment that addresses the predominant export of pharmaceutical products under compulsory licenses.
Congestion-aware Evacuation Routing using Augmented Reality Devices

We present a congestion-aware routing solution for indoor evacuation, which produces real-time individual-customized evacuation routes among multiple destinations while keeping tracks of all evacuees’ locations. A population density map, obtained on-the-fly by aggregating locations of evacuees from user-end Augmented Reality (AR) devices, is used to model the congestion distribution inside a building. To efficiently search the evacuation route among all destinations, a variant of A⋆ algorithm is devised to obtain the optimal solution in a single pass. In a series of simulated studies, we show that the proposed algorithm is more computationally optimized compared to classic path planning algorithms; it generates a more time-efficient evacuation route for each individual that minimizes the overall congestion. A complete system using AR devices is implemented for a pilot study in real-world environments, demonstrating the efficacy of the proposed approach.
Probabilistic Data Association via Mixture Models for Robust Semantic SLAM

Modern robotic systems sense the environment geometrically, through sensors like cameras, lidar, and sonar, as well as semantically, often through visual models learned from data, such as object detectors. We aim to develop robots that can use all of these sources of information for reliable navigation, but each is corrupted by noise. Rather than assume that object detection will eventually achieve near perfect performance across the lifetime of a robot, in this work we represent and cope with the semantic and geometric uncertainty inherent in object detection methods. Specifically, we model data association ambiguity, which is typically non-Gaussian, in a way that is amenable to solution within the common nonlinear Gaussian formulation of simultaneous localization and mapping (SLAM). We do so by eliminating data association variables from the inference process through max-marginalization, preserving standard Gaussian posterior assumptions. The result is a max-mixture-type model that accounts for multiple data association hypotheses. We provide experimental results on indoor and outdoor semantic navigation tasks with noisy odometry and object detection and find that the ability of the proposed approach to represent multiple hypotheses, including the "null" hypothesis, gives substantial robustness advantages in comparison to alternative semantic SLAM approaches.
DeCaf: diagnosing and triaging performance issues in large-scale cloud services

Large scale cloud services use Key Performance Indicators (KPIs) for tracking and monitoring performance. They usually have Service Level Objectives (SLOs) baked into the customer agreements which are tied to these KPIs. Dependency failures, code bugs, infrastructure failures, and other problems can cause performance regressions. It is critical to minimize the time and manual effort in diagnosing and triaging such issues to reduce customer impact. Large volume of logs and mixed type of attributes (categorical, continuous) in the logs makes diagnosis of regressions non-trivial. In this paper, we present the design, implementation and experience from building and deploying DeCaf, a system for automated diagnosis and triaging of KPI issues using service logs. It uses machine learning along with pattern mining to help service owners automatically root cause and triage performance issues. We present the learnings and results from case studies on two large scale cloud services in Microsoft where DeCaf successfully diagnosed 10 known and 31 unknown issues. DeCaf also automatically triages the identified issues by leveraging historical data. Our key insights are that for any such diagnosis tool to be effective in practice, it should a) scale to large volumes of service logs and attributes, b) support different types of KPIs and ranking functions, c) be integrated into the DevOps processes.
PoRCH: A Novel Consensus Mechanism for Blockchain-Enabled Future SCADA Systems in Smart Grids and Industry 4.0

Smart Grids and Industry 4.0 (I4.0) are neither a dream nor a near-future thing anymore, rather it is happening now. The integration of more and more embedded systems and IoT devices is pushing smart grids and I4.0 forward at a breakneck speed. To cope up with this, the modification of age-old SCADA (Supervisory Control and Data Acquisition) systems in terms of decentralization, near-real-time operation, security, and privacy is necessary. In this context, blockchain technology has the potential of providing not only these essential features of the data acquisition process of future SCADA systems but also many other useful add-ons. On the other side, it is evident that various type of security breach tends to take place more during any economic turmoil. These can cause even more serious devastation to the global economy and human life. Thus, it is necessary to make our industries robust, automated, and resilient with secured and immutable data acquiring systems. This paper deals with the implementation scopes of blockchain in the data acquisition part of SCADA systems in the area of the smart grid and I4.0. There are several consensus mechanisms to support blockchain integration in the field cryptocurrencies, vehicular networks, healthcare systems, e-commerce, etc. But little attention has been paid for developing efficient and easy to implement consensus mechanisms in the field of blockchain-enabled SCADA systems. From this perspective, a novel consensus mechanism, which we call PoRCH (Proof of Random Count in Hashes), with a customized mining node selection scheme has been proposed in this paper. Also, a small-scale prototype of a blockchain-enabled data acquisition system has been developed. The performance evaluation of the implemented prototype shows the benefits of blockchain technology.
Mode Surfaces of Symmetric Tensor Fields: Topological Analysis and Seamless Extraction

Mode surfaces are the generalization of degenerate curves and neutral surfaces, which constitute 3D symmetric tensor field topology. Efficient analysis and visualization of mode surfaces can provide additional insight into not only degenerate curves and neutral surfaces, but also how these features transition into each other. Moreover, the geometry and topology of mode surfaces can help domain scientists better understand the tensor fields in their applications. Existing mode surface extraction methods can miss features in the surfaces. Moreover, the mode surfaces extracted from neighboring cells have gaps, which make their subsequent analysis difficult. In this paper, we provide novel analysis on the topological structures of mode surfaces, including a common parameterization of all mode surfaces of a tensor field using 2D asymmetric tensors. This allows us to not only better understand the structures in mode surfaces and their interactions with degenerate curves and neutral surfaces, but also develop an efficient algorithm to seamlessly extract mode surfaces, including neutral surfaces. The seamless mode surfaces enable efficient analysis of their geometric structures, such as the principal curvature directions. We apply our analysis and visualization to a number of solid mechanics data sets.
Nanopolystyrene translocation and fetal deposition after acute lung exposure during late-stage pregnancy

Plastic is everywhere. It is used in food packaging, storage containers, electronics, furniture, clothing, and common single-use disposable items. Microplastic and nanoplastic particulates are formed from bulk fragmentation and disintegration of plastic pollution. Plastic particulates have recently been detected in indoor air and remote atmospheric fallout. Due to their small size, microplastic and nanoplastic particulate in the atmosphere can be inhaled and may pose a risk for human health, specifically in susceptible populations. When inhaled, nanosized particles have been shown to translocate across pulmonary cell barriers to secondary organs, including the placenta. However, the potential for maternal-to-fetal translocation of nanosized-plastic particles and the impact of nanoplastic deposition or accumulation on fetal health remain unknown. In this study we investigated whether nanopolystyrene particles can cross the placental barrier and deposit in fetal tissues after maternal pulmonary exposure. Pregnant Sprague Dawley rats were exposed to 20 nm rhodamine-labeled nanopolystyrene beads (2.64 × 1014 particles) via intratracheal instillation on gestational day (GD) 19. Twenty-four hours later on GD 20, maternal and fetal tissues were evaluated using fluorescent optical imaging. Fetal tissues were fixed for particle visualization with hyperspectral microscopy. Using isolated placental perfusion, a known concentration of nanopolystyrene was injected into the uterine artery. Maternal and fetal effluents were collected for 180 min and assessed for polystyrene particle concentration. Twenty-four hours after maternal exposure, fetal and placental weights were significantly lower (7 and 8%, respectively) compared with controls. Nanopolystyrene particles were detected in the maternal lung, heart, and spleen. Polystyrene nanoparticles were also observed in the placenta, fetal liver, lungs, heart, kidney, and brain suggesting maternal lung-to-fetal tissue nanoparticle translocation in late stage pregnancy. These studies confirm that maternal pulmonary exposure to nanopolystyrene results in the translocation of plastic particles to placental and fetal tissues and renders the fetoplacental unit vulnerable to adverse effects. These data are vital to the understanding of plastic particulate toxicology and the developmental origins of health and disease.
OBSTRUCTING CHIARI NETWORK FACILITATING BLOOD FLOW ACROSS A PATENT FORAMEN OVALE CAUSING HYPOXIA

A Chiari network (CN) is a serpiginous mobile structure in the right atrium resulting from incomplete resorption of the sinus venosus (right horn) during embryonic development that is present in 2-3% of autopsies and usually benign. Failure of sinus venosus structures to regress can result in a
Design Space Reparameterization Enforces Hard Geometric Constraints in Inverse-Designed Nanophotonic Devices

Inverse design algorithms are the basis for realizing high-performance, freeform nanophotonic devices. Current methods to enforce geometric constraints, such as practical fabrication constraints, a...
JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method

We introduce a new large scale unconstrained crowd counting dataset (JHU-CROWD++) that contains "4,372" images with "1.51 million" annotations. In comparison to existing datasets, the proposed dataset is collected under a variety of diverse scenarios and environmental conditions. Specifically, the dataset includes several images with weather-based degradations and illumination variations, making it a very challenging dataset. Additionally, the dataset consists of a rich set of annotations at both image-level and head-level. Several recent methods are evaluated and compared on this dataset. The dataset can be downloaded from http://www.crowd-counting.com. Furthermore, we propose a novel crowd counting network that progressively generates crowd density maps via residual error estimation. The proposed method uses VGG16 as the backbone network and employs density map generated by the final layer as a coarse prediction to refine and generate finer density maps in a progressive fashion using residual learning. Additionally, the residual learning is guided by an uncertainty-based confidence weighting mechanism that permits the flow of only high-confidence residuals in the refinement path. The proposed Confidence Guided Deep Residual Counting Network (CG-DRCN) is evaluated on recent complex datasets, and it achieves significant improvements in errors.
A general relativistic model for magnetic monopole-infused compact objects

Emergent concepts from astroparticle physics are incorporated into a classical solution of the Einstein-Maxwell equations for a binary magnetohydrodynamic fluid, in order to describe the final equilibrium state of compact objects infused with magnetic monopoles produced by proton-proton collisions within the intense dipolar magnetic fields generated by these objects during their collapse. It is found that the effective mass of such an object’s acquired monopolar magnetic field is three times greater than the mass of its native fluid and monopoles combined, necessitating that the interior matter undergo a transition to a state of negative pressure in order to attain equilibrium. Assuming full symmetry between the electric and magnetic Maxwell equations yields expressions for the monopole charge density and magnetic field by direct analogy with their electrostatic equivalents; inserting these into the Einstein equations then leads to an interior metric which is well-behaved from the origin to the surface, where it matches smoothly to an exterior magnetic Reissner-Nordstrom metric free of any coordinate pathologies. The source fields comprising the model are all described by simple, well-behaved polynomial functions of the radial coordinate, and are combined with straightforward regularity conditions to yield expressions delimiting several fundamental physical parameters pertaining to this hypothetical astrophysical object.
A Fast Summation Method for Oscillatory Lattice Sums.

We present a fast summation method for lattice sums of the type which arise when solving wave scattering problems with periodic boundary conditions. While there are a variety of effective algorithms in the literature for such calculations, the approach presented here is new and leads to a rigorous analysis of Wood’s anomalies. These arise when illuminating a grating at specific combinations of the angle of incidence and the frequency of the wave, for which the lattice sums diverge. They were discovered by Wood in 1902 as singularities in the spectral response. The primary tools in our approach are the Euler-Maclaurin formula and a steepest descent argument. The resulting algorithm has super-algebraic convergence and requires only milliseconds of CPU time.
Chiral perturbation theory on the lattice: Strong coupling expansion

Abstract We evaluate the coefficients of the effective chiral lagrangian to O( p 4 ) in the strong-coupling, large- N expansion. In this limit we explicitly perform the functional integral over fundamental degrees of freedom and obtain the effective chiral lagrangian. We perform this calculation on the Body-Centered Hypercubical lattice which preserves euclidean invariance to order p 4 . We further discuss how the coefficients could be obtained numerically, out of the strong-coupling domain.
Magnetic Domains in Magnetar Matter as an Engine for Soft Gamma-ray Repeaters and Anomalous X-ray Pulsars

Magnetars have been suggested as the most promising site for the origin of observed soft gamma-ray repeaters (SGRs) and anomalous X-ray pulsars (AXPs). In this work, we investigate the possibility that SGRs and AXPs might be observational evidence for a magnetic phase separation in magnetars. We study magnetic domain formation as a new mechanism for SGRs and AXPs in which magnetar matter separates into phases containing different flux densities. We identify the parameter space in matter density and magnetic field strength at which there is an instability for magnetic domain formation. We conclude that such instabilities will likely occur in the deep outer crust for the magnetic Baym, Pethick, and Sutherland model and in the inner crust and core for magnetars described in the relativistic Hartree theory. Moreover, we estimate that the energy released by the onset of this instability is comparable with the energy emitted by SGRs.
Remnants, fuzzballs or wormholes?

The black hole information paradox has caused enormous confusion over four decades. But in recent years, the theorem of quantum strong-subadditivity has sorted out the possible resolutions into three sharp categories: (i) No new physics at r ≫ lp; this necessarily implies remnants/information loss. A realization of remnants is given by a baby universe attached near r ~ 0. (ii) Violation of the "no-hair" theorem by nontrivial effects at the horizon r ~ M. This possibility is realized by fuzzballs in string theory, and gives unitary evaporation. (iii) Having the vacuum at the horizon, but requiring that Hawking quanta at r ~ M3 be somehow identified with degrees of freedom inside the black hole. A model for this "extreme nonlocality" is realized by conjecturing that wormholes connect the radiation quanta to the hole.
Exact bosonization in arbitrary dimensions

The author provides a method to map any fermionic system in any dimension to a spin system, which considers the spin system as a Z${}_{2}$ gauge theory with a modified gauge constraint.
On volume-preserving vector fields and finite-type invariants of knots

We consider the general non-vanishing, divergence-free vector fields defined on a domain in -space and tangent to its boundary. Based on the theory of finite-type invariants, we define a family of invariants for such fields, in the style of Arnold’s asymptotic linking number. Our approach is based on the configuration space integrals due to Bott and Taubes.
Max-pooling loss training of long short-term memory networks for small-footprint keyword spotting

We propose a max-pooling based loss function for training Long Short-Term Memory (LSTM) networks for small-footprint keyword spotting (KWS), with low CPU, memory, and latency requirements. The max-pooling loss training can be further guided by initializing with a cross-entropy loss trained network. A posterior smoothing based evaluation approach is employed to measure keyword spotting performance. Our experimental results show that LSTM models trained using cross-entropy loss or max-pooling loss outperform a cross-entropy loss trained baseline feed-forward Deep Neural Network (DNN). In addition, max-pooling loss trained LSTM with randomly initialized network performs better compared to cross-entropy loss trained LSTM. Finally, the max-pooling loss trained LSTM initialized with a cross-entropy pre-trained network shows the best performance, which yields 67:6% relative reduction compared to baseline feed-forward DNN in Area Under the Curve (AUC) measure.
Modified interior-point method for large-and-sparse low-rank semidefinite programs

Semidefinite programs (SDPs) are powerful theoretical tools that have been studied for over two decades, but their practical use remains limited due to computational difficulties in solving large-scale, realistic-sized problems. In this paper, we describe a modified interior-point method for the efficient solution of large-and-sparse low-rank SDPs, which finds applications in graph theory, approximation theory, control theory, sum-of-squares, etc. Given that the problem data is large-and-sparse, conjugate gradients (CG) can be used to avoid forming, storing, and factoring the large and fully-dense interior-point Hessian matrix, but the resulting convergence rate is usually slow due to ill-conditioning. Our central insight is that, for a rank-k, size-n SDP, the Hessian matrix is ill-conditioned only due to a rank-nk perturbation, which can be explicitly computed using a size-n eigendecomposition. We construct a preconditioner to “correct” the low-rank perturbation, thereby allowing preconditioned CG to solve the Hessian equation in a few tens of iterations. This modification is incorporated within SeDuMi, and used to reduce the solution time and memory requirements of large-scale matrix-completion problems by several orders of magnitude.
Additional cases of positive twisted torus knots

A twisted torus knot is a knot obtained from a torus knot by twisting adjacent strands by full twists. The twisted torus knots lie in F, the genus 2 Heegaard surface for S3. Primitive/primitive and primitive/Seifert knots lie in F in a particular way. Dean gives sufficient conditions for the parameters of the twisted torus knots to ensure they are primitive/primitive or primitive/Seifert. Using Dean’s conditions, Doleshal shows that there are infinitely many twisted torus knots that are fibered and that there are twisted torus knots with distinct primitive/Seifert representatives with the same slope in F. In this paper, we extend Doleshal’s results to show there is a four parameter family of positive twisted torus knots. Additionally, we provide new examples of twisted torus knots with distinct representatives with the same surface slope in F.
String Theory at LHC Using Higgs Production and Decay From String Balls

We study Higgs production and decay from TeV scale string balls in pp collisions at \( \sqrt {s} = 14\;{\text{TeV}} \) at LHC. We present the results of total cross section of diphotons, invariant mass distribution of the diphotons and pT distribution of the diphotons and ZZ pairs from Higgs from string balls at LHC. We find that the invariant mass distribution of diphotons from Higgs from string balls is not very sensitive to the increase in diphoton invariant mass. We find that for string mass scale Ms=2.5 TeV, which is the lower limit of the string mass scale set by the recent CMS collaboration at LHC, the \( \frac{{d\sigma }}{{d{p_T}}} \) of high pT (≥450 GeV) diphotons and ZZ pairs produced from Higgs from string balls is larger than that from standard model Higgs. Hence in the absence of black hole production at LHC an enhancement of high pT diphotons and ZZ pairs at LHC can be useful signatures for string theory at LHC. Since the matrix element for Higgs production in parton-parton collisions via string regge excitations is not available we compute \( \frac{{d\sigma }}{{d{p_T}}} \) of photon production from string regge excitations and make a comparison with that from string balls at LHC. We find that for string mass scale Ms = 2.5 TeV the \( \frac{{d{\sigma_{photon}}}}{{d{p_T}}} \) from string regge excitations is larger than that from string balls and black holes at LHC.
The Crab Pulsar at Centimeter Wavelengths. I. Ensemble Characteristics

We have observed the pulsar in the Crab Nebula at high radio frequencies and high time resolution. We present continuously sampled data at 640 ns time resolution and individual bright pulses recorded at down to 0.25 ns time resolution. Combining our new data with previous data from our group and from the literature shows the dramatic changes in the pulsar’s radio emission between low and high radio frequencies. Below about 5 GHz the mean profile is dominated by the bright Main Pulse and Low-Frequency Interpulse. Everything changes, however, above about 5 GHz; the Main Pulse disappears and the mean profile of the Crab pulsar is dominated by the High-Frequency Interpulse (which is quite different from its low-frequency counterpart) and the two High-Frequency Components. We present detailed observational characteristics of these different components which future models of the pulsar’s magnetosphere must explain.
Orbital-dependent effects of electron correlations in microscopic models for iron-based superconductors

Abstract The bad metal behavior in the normal state of the iron-based superconductors suggests an intimate connection between the superconductivity and a proximity to a Mott transition. At the same time, there is strong evidence for the multiorbital nature of the electronic excitations. It is then important to understand the orbital-dependent effects of electron correlations. In this paper we review the recent theoretical progresses on the metal-to-insulator transition in multiorbital models for the iron-based superconductors. These include studies of models that contain at least the 3 d xy and 3 d xz / yz models, using a slave-spin technique. For commensurate filling corresponding to that of the parent iron pnictides and chalcogenideds, a Mott transition generally exists in all these models. Near the Mott transition, a strongly correlated metal exhibiting bad metal features and strong orbital selectivity is stabilized due to the interplay of Hund’s coupling and orbital-degeneracy breaking. Particularly for the alkaline iron selenides, the ordered vacancies effectively reduce the kinetic energy, thereby pushing the system further into the Mott-insulating regime; in the metallic state, there exists an orbital-selective Mott phase in which the iron 3 d xy orbital is Mott localized while the other 3 d orbitals are still itinerant. An overall phase diagram for the alkaline iron selenides has been proposed, in which the orbital-selective Mott phase connects between the superconducting phase and the Mott-insulating parent state.
An X-ray view of radio sources

We review recent examples where the synergy between radio and X-ray observations has led to substantial progress in understanding astronomical systems. The sub-arcsecond imaging capabilities of the Chandra X-ray observatory provides a 100-fold improvement for com- paring X-ray and radio structures. We specifically discuss examples which provide insight into the outflow of material and energy from pul- sars and supernovae, the centers of clusters of galaxies, and the nuclei of quasars.
Almost inert Higgs bosons at the LHC

Non-minimal Higgs sectors are strongly constrained by the agreement of the measured couplings of the 125 GeV Higgs with Standard Model predictions. This agreement can be explained by an approximate ℤ2 symmetry under which the additional Higgs bosons are odd. This allows the additional Higgs bosons to be approximately inert, meaning that they have suppressed VEVs and suppressed mixing with the Standard Model Higgs. In this case, single production of the new Higgs bosons is suppressed, but electroweak pair production is unsuppressed. We study the phenomenology of a minimal 2 Higgs doublet model that realizes this scenario. In a wide range of parameters, the phenomenology of the model is essentially fixed by the masses of the exotic Higgs bosons, and can therefore be explored systematically. We study a number of different plausible signals in this model, and show that several LHC searches can constrain or discover additional Higgs bosons in this parameter space. We find that the reach is significantly extended at the high luminosity LHC.
Lattice-constant and band-gap tuning in wurtzite and zincblende BInGaN alloys

InGaN light-emitting diodes (LEDs) are more efficient and cost effective than incandescent and fluorescent lighting, but lattice mismatch limits the thickness of InGaN layers that can be grown on GaN without performance-degrading dislocations. In this work, we apply hybrid density functional theory calculations to investigate the thermodynamic stability, lattice parameters, and bandgaps of wurtzite and zincblende quaternary BInGaN alloys. We find that the wurtzite phase is more stable and can be lattice matched to GaN for BInGaN compositions containing up to ∼30% boron. The lattice match with GaN decreases strain and enables thicker active layers that mitigate Auger recombination and increase the efficiency of the LEDs. The bandgap of the alloy remains tunable throughout the visible spectrum. Our results indicate that BInGaN alloys are promising alternatives to InGaN for high-efficiency, high-power LEDs.
Differential Geometry and Mechanics: Applications to Chaotic Dynamical Systems

The aim of this article is to highlight the interest to apply Differential Geometry and Mechanics concepts to chaotic dynamical systems study. Thus, the local metric properties of curvature and torsion will directly provide the analytical expression of the slow manifold equation of slow-fast autonomous dynamical systems starting from kinematics variables (velocity, acceleration and over-acceleration or jerk). The attractivity of the slow manifold will be characterized thanks to a criterion proposed by Henri Poincare. Moreover, the specific use of acceleration will make it possible on the one hand to define slow and fast domains of the phase space and on the other hand, to provide an analytical equation of the slow manifold towards which all the trajectories converge. The attractive slow manifold constitutes a part of these dynamical systems attractor. So, in order to propose a description of the geometrical structure of attractor, a new manifold called singular manifold will be introduced. Various applications of this new approach to the models of Van der Pol, cubic-Chua, Lorenz, and Volterra–Gause are proposed.