cssi/SciSciGPT-SciSciNet huggingface dataset:
Detection of false data injection attacks in smart grid under colored Gaussian noise

In this paper, we consider the problems of state estimation and false data injection detection in smart grid when the measurements are corrupted by colored Gaussian noise. By modeling the noise with the autoregressive process, we estimate the state of the power transmission networks and develop a generalized likelihood ratio test (GLRT) detector for the detection of false data injection attacks. We show that the conventional approach with the assumption of Gaussian noise is a special case of the proposed method, and thus the new approach has more applicability. The proposed detector is also tested on an independent component analysis (ICA) based unobservable false data attack scheme that utilizes similar assumptions of sample observation. We evaluate the performance of the proposed state estimator and attack detector on the IEEE 30-bus power system with comparison to conventional Gaussian noise based detector. The superior performance of both observable and unobservable false data attacks demonstrates the effectiveness of the proposed approach and indicates a wide application on the power signal processing.
On generalized quadrangles with a point regular group of automorphisms

Abstract A generalized quadrangle is a point-line incidence geometry such that any two points lie on at most one line and, given a line l and a point P not incident with l , there is a unique point of l collinear with P . We study the structure of groups acting regularly on the point set of a generalized quadrangle. In particular, we provide a characterization of the generalized quadrangles with a group of automorphisms acting regularly on both the point set and the line set and show that such a thick generalized quadrangle does not admit a polarity. Moreover, we prove that a group G acting regularly on the point set of a generalized quadrangle of order ( u 2 , u 3 ) or ( s , s ) , where s is odd and s + 1 is coprime to 3, cannot have any nonabelian minimal normal subgroups.
Lattice Points in Algebraic Cross-polytopes and Simplices

The number of lattice points $$| tP \cap \mathbb {Z}^d |$$ , as a function of the real variable $$t>1$$ is studied, where $$P \subset \mathbb {R}^d$$ belongs to a special class of algebraic cross-polytopes and simplices. It is shown that the number of lattice points can be approximated by an explicitly given polynomial of t depending only on P. The error term is related to a simultaneous Diophantine approximation problem for algebraic numbers, as in Schmidt’s theorem. The main ingredients of the proof are a Poisson summation formula for general algebraic polytopes, and a representation of the Fourier transform of the characteristic function of an arbitrary simplex in the form of a complex line integral.
Weyl Modules and Opers without Monodromy

We prove that the algebra of endomorphisms of a Weyl module of critical level is isomorphic to the algebra of functions on the space of monodromy-free opers on the disc with regular singularity and residue determined by the highest weight of the Weyl module. This result may be used to test the local geometric Langlands correspondence proposed in our earlier work.
How Hard is Robust Mean Estimation

Robust mean estimation is the problem of estimating the mean $\mu \in \mathbb{R}^d$ of a $d$-dimensional distribution $D$ from a list of independent samples, an $\epsilon$-fraction of which have been arbitrarily corrupted by a malicious adversary. Recent algorithmic progress has resulted in the first polynomial-time algorithms which achieve \emph{dimension-independent} rates of error: for instance, if $D$ has covariance $I$, in polynomial-time one may find $\hat{\mu}$ with $\|\mu - \hat{\mu}\| \leq O(\sqrt{\epsilon})$. However, error rates achieved by current polynomial-time algorithms, while dimension-independent, are sub-optimal in many natural settings, such as when $D$ is sub-Gaussian, or has bounded $4$-th moments. In this work we give worst-case complexity-theoretic evidence that improving on the error rates of current polynomial-time algorithms for robust mean estimation may be computationally intractable in natural settings. We show that several natural approaches to improving error rates of current polynomial-time robust mean estimation algorithms would imply efficient algorithms for the small-set expansion problem, refuting Raghavendra and Steurer's small-set expansion hypothesis (so long as $P \neq NP$). We also give the first direct reduction to the robust mean estimation problem, starting from a plausible but nonstandard variant of the small-set expansion problem.
Collision of eigenvalues for matrix-valued processes

We examine the probability that at least two eigenvalues of a Hermitian matrix-valued Gaussian process, collide. In particular, we determine sharp conditions under which such probability is zero. A...
Osteogenesis Imperfecta: Skeletal Outcomes After Bisphosphonate Discontinuation at Final Height.

Intravenous cyclical bisphosphonates are widely used to treat children with moderate to severe osteogenesis imperfecta (OI). Bisphosphonates are often discontinued when growth is completed, but subsequent skeletal changes have not been studied in detail. We assessed 31 patients (22 females) with OI who had started intravenous bisphosphonates (either pamidronate or zoledronic acid) before 13 years of age, were treated for at least 2 years (range 4.7-15.7 years), and discontinued treatment after completion of growth, when their age ranged from 13.4 to 20.0 years (mean 16.4 years). At 4 years after treatment discontinuation, lumbar spine areal bone mineral density (BMD) had increased by 4% (p < 0.05). Peripheral quantitative computed tomography of the radius showed a decrease in trabecular volumetric BMD at the distal metaphysis of 19% but an increase in cortical volumetric BMD of 4% (p < 0.05 for both). None of the patients sustained a new vertebral compression fracture during follow-up. The proportion of patients with new long-bone fractures was higher in the 2 years before treatment discontinuation than in the last 2 years of follow-up (42% and 16%, respectively; p < 0.05). © 2019 American Society for Bone and Mineral Research.
Boundaries of Baumslag–Solitar groups

A Z–structure on a group G was introduced by Bestvina in order to extend the notion of a group boundary beyond the realm of CAT(0) and hyperbolic groups. A refinement of this notion, introduced by Farrell and Lafont, includes a G–equivariance requirement, and is known as an ℰZ–structure. The general questions of which groups admit Z– or ℰZ–structures remain open. Here we show that all Baumslag–Solitar groups admit ℰZ–structures and all generalized Baumslag–Solitar groups admit Z–structures.
Software metrics for aiding program development and debugging

Computer program graphs have proven very useful because they illuminate the structural characteristics of a program. Structural characteristics, as a representation of program complexity, have been shown to be strongly related to program development time, program quality and difficulty of debugging. 1 – 3 The use of graphs for these purposes is not widely known or understood in the data processing community. It is the aim of this paper to provide an introduction to graphs as they apply to program representation and to show examples of their use in program design and debugging.
Adaptive Realization of Linear Closed-Loop Tracking Dynamics in the Presence of Large System Model Errors

A novel adaptive feedback control approach for nonlinear mechanical systems is presented. The approach applies to nonlinear trajectory tracking and has the remarkable property that the tracking error dynamics asymptotically approach a specified linear PID response for the case where the external disturbances are constant. The methodology applies to a large class of nonlinear mechanical systems, however, it is illustrated for the case of nonlinear rigid body maneuvers subject to actuator saturation constraints and large uncertainty of the system mass and inertia properties. While the system mass or inertias are not identified in this approach, the external disturbances are accurately estimated if they are constant or slowly varying with respect to the adaptation rate. A benefit of this method is that it requires no a priori knowledge of the unknown system parameters or bounds thereof.
Aluminum-26 chronology of dust coagulation and early solar system evolution

Dust condensation and coagulation in the early solar system are the first steps toward forming the terrestrial planets, but the time scales of these processes remain poorly constrained. Through isotopic analysis of small Ca-Al–rich inclusions (CAIs) (30 to 100 μm in size) found in one of the most pristine chondrites, Allan Hills A77307 (CO3.0), for the short-lived 26Al-26Mg [t1/2 = 0.72 million years (Ma)] system, we have identified two main populations of samples characterized by well-defined 26Al/27Al = 5.40 (±0.13) × 10−5 and 4.89 (±0.10) × 10−5. The result of the first population suggests a 50,000-year time scale between the condensation of micrometer-sized dust and formation of inclusions tens of micrometers in size. The 100,000-year time gap calculated from the above two 26Al/27Al ratios could also represent the duration for the Sun being a class I source.
Overlapping and unique neural circuits support perceptual decision making and confidence

Perceptual decision making is the process of making a judgment about the identity of a stimulus based on the available sensory information. Perceptual decisions are naturally accompanied by a sense of confidence in the accuracy of the decision. Although the neural correlates of perceptual decision making and confidence have been the subject of substantial research, there is disagreement on whether they are supported by the same or different neural circuits. To address this question, we designed a functional MRI experiment that temporally separated the processes of perceptual decision making and confidence by presenting the confidence scale only after the offset of the decision. Additionally, we decorrelated the regressors for decision and confidence by slowing down participants responses and presenting the confidence question only on a fraction of the trials. We found that perceptual decision making and confidence were supported by large and mostly overlapping brain circuits including frontal, parietal, posterior, and cingulate regions. Further, confidence recruited a number of unique regions, whereas there was no evidence for perceptual decision making recruiting unique regions not involved in confidence. These results demonstrate that the neural circuits supporting perceptual decision making and confidence have a very high degree of overlap, and suggest a largely shared computational mechanisms with unique components for confidence but not for perceptual judgments.
eIF5B gates the transition from translation initiation to elongation.

Translation initiation determines both the quantity and identity of the protein that is encoded in an mRNA by establishing the reading frame for protein synthesis. In eukaryotic cells, numerous translation initiation factors prepare ribosomes for polypeptide synthesis; however, the underlying dynamics of this process remain unclear1,2. A central question is how eukaryotic ribosomes transition from translation initiation to elongation. Here we use in vitro single-molecule fluorescence microscopy approaches in a purified yeast Saccharomyces cerevisiae translation system to monitor directly, in real time, the pathways of late translation initiation and the transition to elongation. This transition was slower in our eukaryotic system than that reported for Escherichia coli3–5. The slow entry to elongation was defined by a long residence time of eukaryotic initiation factor 5B (eIF5B) on the 80S ribosome after the joining of individual ribosomal subunits—a process that is catalysed by this universally conserved initiation factor. Inhibition of the GTPase activity of eIF5B after the joining of ribosomal subunits prevented the dissociation of eIF5B from the 80S complex, thereby preventing elongation. Our findings illustrate how the dissociation of eIF5B serves as a kinetic checkpoint for the transition from initiation to elongation, and how its release may be governed by a change in the conformation of the ribosome complex that triggers GTP hydrolysis. Single-molecule dynamics reveal that the GTPase activity of eukaryotic initiation factor eIF5B serves as a kinetic checkpoint for the transition from translation initiation to elongation.
Spatial Information and the Legibility of Urban Form: Big Data in Urban Morphology

Abstract Urban planning and morphology have relied on analytical cartography and visual communication tools for centuries to illustrate spatial patterns, conceptualize proposed designs, compare alternatives, and engage the public. Classic urban form visualizations – from Giambattista Nolli’s ichnographic maps of Rome to Allan Jacobs’s figure-ground diagrams of city streets – have compressed physical urban complexity into easily comprehensible information artifacts. Today we can enhance these traditional workflows through the Smart Cities paradigm of understanding cities via user-generated content and harvested data in an information management context. New spatial technology platforms and big data offer new lenses to understand, evaluate, monitor, and manage urban form and evolution. This paper builds on the theoretical framework of visual cultures in urban planning and morphology to introduce and situate computational data science processes for exploring urban fabric patterns and spatial order. It demonstrates these workflows with OSMnx and data from OpenStreetMap, a collaborative spatial information system and mapping platform, to examine street network patterns, orientations, and configurations in different study sites around the world, considering what these reveal about the urban fabric. The age of ubiquitous urban data and computational toolkits opens up a new era of worldwide urban form analysis from integrated quantitative and qualitative perspectives.
Knitting Skeletons: A Computer-Aided Design Tool for Shaping and Patterning of Knitted Garments

This work presents a novel interactive system for simple garment composition and surface patterning. Our approach makes it easier for casual users to customize machine-knitted garments, while enabling more advanced users to design their own composable templates. Our tool combines ideas from CAD software and image editing: it allows the composition of (1) parametric knitted primitives, and (2) stitch pattern layers with different resampling behaviours. By leveraging the regularity of our primitives, our tool enables interactive customization with automated layout and real-time patterning feedback. We show a variety of garments and patterns created with our tool, and highlight our ability to transfer shape and pattern customizations between users.
Supercontinuum generation in varying dispersion and birefringent silicon waveguide

Ability to selectively enhance the amplitude and maintain high coherence of the supercontinuum signal with long pulses is gaining significance. In this work, an extra degree of freedom afforded by varying the dispersion profile of a waveguide is utilized to selectively enhance supercontinuum. As much as 16 dB signal enhancement in the telecom window and 100 nm of wavelength extension is achieved with a cascaded waveguide, compared to a fixed dispersion waveguide. Waveguide tapering, in particular with increasing width, is determined to have a flatter and more coherent supercontinuum than a fixed dispersion waveguide when longer input pulses are used. Furthermore, due to the strong birefringence of an asymmetric silicon waveguide the supercontinuum signal is broadened by pumping simultaneously with both quasi-transverse electric (TE) and quasi-transverse magnetic (TM) mode in the anomalous dispersion regime. Thus, selective signal generation is obtained by controlling the dispersion for the two modes. Such waveguides offer several advantages over optical fiber as the variation in dispersion can be controlled with greater flexibility in an integrated platform. This work paves the way forward for various applications in fields ranging from medicine to telecom where specific wavelength windows need to be targeted.
A Mixture of Linear-Linear Regression Models for Linear-Circular Regression

AbstractWe introduce a new approach to a linear-circular regression problem that relates multiple linear predictors to a circular response. We follow a modelling approach of a wrapped normal distri...
Computational framework to capture the spatiotemporal density of cells with a cumulative environmental coupling

Abstract Stochastic agent-based models can account for millions of cells with spatiotemporal movement that can be a function of different factors. However, these simulations can be computationally expensive. In this work, we develop a novel computational framework to describe and simulate stochastic cellular processes that are coupled to the environment. Specifically, through upscaling, we derive a continuum governing equation that considers the cell density as a function of time, space, and a cumulative variable that is coupled to the environmental conditions. For this new governing equation, we consider the stability through an energy analysis, as well as proving uniqueness and well-posedness. To solve the governing equations in free-space, we propose a numerical method using fundamental solutions. As an application, we study a cell moving in an infinite domain that contains a toxic chemical, where a cumulative exposure above a critical value results in cell death. We illustrate the validity of this new modeling framework and associated numerical methods by comparing the density of live cells to results from the corresponding agent-based model.
Moving the Needle in MMA: On the Marginal Revenue Product of UFC Fighters:

This article analyzes fighter marginal products (MP) and marginal revenue products (MRP) for the largest component of Ultimate Fighting Championship (UFC) revenues: content. Most bouts are fought i...
On Heavy-user Bias in A/B Testing

On-line experimentation (also known as A/B testing) has become an integral part of software development. To timely incorporate user feedback and continuously improve products, many software companies have adopted the culture of agile deployment, requiring online experiments to be conducted and concluded on limited sets of users for a short period. While conceptually efficient, the result observed during the experiment duration can deviate from what is seen after the feature deployment, which makes the A/B test result biased. In this paper, we provide theoretical analysis to show that heavy-users can contribute significantly to the bias, and propose a re-sampling estimator for bias adjustment.
The complexity of explaining neural networks through (group) invariants

Abstract Ever since the work of Minsky and Papert, it has been thought that neural networks derive their effectiveness by finding representations of the data that are invariant with respect to the task. In other words, the representations eliminate components of the data that vary in a way that is irrelevant. These invariants are naturally expressed with respect to group operations, and thus an understanding of these groups is key to explaining the effectiveness of the neural network. Moreover, a line of work in deep learning has shown that explicit knowledge of group invariants can lead to more effective training results. In this paper, we investigate the difficulty of discovering anything about these implicit invariants. Unfortunately, our main results are negative: we show that a variety of questions around investigating invariant representations are NP-hard, even in approximate settings. Moreover, these results do not depend on the kind of architecture used: in fact, our results follow as soon as the network architecture is powerful enough to be universal. The key idea behind our results is that if we can find the symmetries of a problem then we can solve it.
Robust And Interpretable Blind Image Denoising Via Bias-Free Convolutional Neural Networks

We study the generalization properties of deep convolutional neural networks for image denoising in the presence of varying noise levels. We provide extensive empirical evidence that current state-of-the-art architectures systematically overfit to the noise levels in the training set, performing very poorly at new noise levels. We show that strong generalization can be achieved through a simple architectural modification: removing all additive constants. The resulting "bias-free" networks attain state-of-the-art performance over a broad range of noise levels, even when trained over a limited range. They are also locally linear, which enables direct analysis with linear-algebraic tools. We show that the denoising map can be visualized locally as a filter that adapts to both image structure and noise level. In addition, our analysis reveals that deep networks implicitly perform a projection onto an adaptively-selected low-dimensional subspace, with dimensionality inversely proportional to noise level, that captures features of natural images.
Soil loss from small rangeland plots under simulated rainfall and run-on conditions

Abstract In order to predict and mitigate soil erosion it is necessary to understand the mechanism of detachment by raindrops and by shallow flow. The relative contribution of these drivers to the overall erosion process on arid rangelands is not well understood and field experimental data is limited. The experiment was conducted using simulated rainfall and overland flow on 56 small (2 m × 6 m) natural plots located on 7 rangeland sites in Arizona, USA. The goals of the study were (i) to compare raindrop driven and shallow flow driven erosion rates on arid rangeland, and (ii) assess the role of flow hydraulics, vegetation, and cover on attenuation of erosive impact of raindrops. A total of 520 measurements of steady state flow under two treatments (rainfall and run-on) were obtained. Flow discharge on the plots varied between 3 and 355 mm h−1 and unit sediment yield varied between 0.02 and 30 g m−2 min−1. Sediment yield was best predicted by flow discharge for both rainfall and run-on treatments on all sites explaining 18% to 75% of its variability. There was statistically significant difference between two treatments. Rainfall treatment generated 2 to 44 times more sediment than run-on at the same discharge rate. We found no strong evidence of raindrop impact affecting overland flow velocity. Among 19 variables related to surface conditions a weak correlation was found between sediment yield and plant foliar cover, structure, and surface litter. However, there was no single best cover predictor common for all ecological sites tested.
Your Hometown Matters: Popularity-Difference Bias in Online Reputation Platforms

We study a new source of bias in online review platforms that originates from the popularity difference between the traveling reviewer’s hometown and destination.
Patient Derived Models to Study Head and Neck Cancer Radiation Response

Patient-derived model systems are important tools for studying novel anti-cancer therapies. Patient-derived xenografts (PDXs) have gained favor over the last 10 years as newer mouse strains have improved the success rate of establishing PDXs from patient biopsies. PDXs can be engrafted from head and neck cancer (HNC) samples across a wide range of cancer stages, retain the genetic features of their human source, and can be treated with both chemotherapy and radiation, allowing for clinically relevant studies. Not only do PDXs allow for the study of patient tissues in an in vivo model, they can also provide a renewable source of cancer cells for organoid cultures. Herein, we review the uses of HNC patient-derived models for radiation research, including approaches to establishing both orthotopic and heterotopic PDXs, approaches and potential pitfalls to delivering chemotherapy and radiation to these animal models, biological advantages and limitations, and alternatives to animal studies that still use patient-derived tissues.
Debreach: mitigating compression side channels via static analysis and transformation

Compression is an emerging source of exploitable side-channel leakage that threatens data security, particularly in web applications where compression is indispensable for performance reasons. Current approaches to mitigating compression side channels have drawbacks in that they either degrade compression ratio drastically or require too much effort from developers to be widely adopted. To bridge the gap, we develop Debreach, a static analysis and program transformation based approach to mitigating compression side channels. Debreach consists of two steps. First, it uses taint analysis to soundly identify flows of sensitive data in the program and uses code instrumentation to annotate data before feeding them to the compressor. Second, it enhances the compressor to exploit the freedom to not compress of standard compression protocols, thus removing the dependency between sensitive data and the size of the compressor's output. Since Debreach automatically instruments applications and does not change the compression protocols, it has the advantage of being non-disruptive and compatible with existing systems. We have evaluated Debreach on a set of web server applications consisting of 145K lines of PHP code. Our experiments show that, while ensuring leakage-freedom, Debreach can achieve significantly higher compression performance than state-of-the-art approaches.
The Innate Immune Sensor Sting Promotes Donor CD8+ T Cell Activation and Recipient APC Death Early after Preclinical Allogeneic Hematopoietic Stem Cell Transplantation

Graft-versus-host disease (GVHD) remains a significant cause of morbidity and mortality in patients receiving allogeneic hematopoietic stem cell transplants (aHSCTs). Pre-HSCT chemoradiation results in the death of dividing cells and release of endogenous danger signals. These molecules drive the activation of antigen presenting cells (APCs) and the differentiation of allo-reactive donor T cells. We identified a role for Stimulator of Interferon Genes (STING), an innate immune sensor, in GVHD using pre-clinical MHC-matched unrelated donor (MUD) HSCT models. Here we show that STING rapidly promotes donor CD8+ T cell activation and recipient APC death early after aHSCT. Post MUD HSCT, we previously found a >2x reduction in IFNβ, TNFα and IL-6 mRNA in STING−/− vs WT recipient colonic mRNA expression (48 hrs) as well as decreased weight loss, GVHD scores and skin pathology (6 wks) vs WT. Chimeric studies showed that STING loss in non-hematopoietic cells induced this effect. Conversely, we recently found a single early ( D100), dose of the STING agonist DMXAA increased GVHD scores and lethality in WT, but not STING−/−, recipients. Thus, the activation of this pathway can promote GVHD following MUD HSCT. Furthermore, mice homozygous for a murine homologue of a human allele associated with diminished STING activity (STINGHAQ/HAQ) also exhibited reduced GVHD after MUD HSCT, suggesting potential clinical importance. Interestingly, our findings that STING deficiency ameliorates GVHD in MUD HSCT (Bader CS, et al, in revision Sci. Transl. Med.) contrasts reported observations that STING deficiency can exacerbate GVHD after MHC-mismatched (MMUD) HSCT. Since CD4+ and CD8+ T cells are central in MMUD and MUD GVHD, respectively, we transplanted MMUD BALB/c BM + CD8+ T cells into B6-WT and STING−/− mice and notably, found that STING−/- recipients now developed reduced GVHD clinical scores, skin pathology and frequencies of activated T cells 8 wks post-HSCT vs WT. We also found that STING−/− mice had greater numbers of recipient splenic CD11b+CD11c+ APCs and these cells expressed reduced MHC I protein 1 day after MMUD B6 into BALB/c aHSCT vs WT (Fig. A, B). Moreover, STING−/− recipient spleens contained lower numbers of donor CD8+ T cells producing IFNγ and TNFα (Fig. C), supporting the hypothesis that STING contributes to early activation of donor CD8+ T cells and loss of recipient APCs. Next, to identify if reduced host MHC II+ APCs affected donor CD4+ T cell activation, B6-Nur77GFP T cells were used to explicitly monitor T cell receptor signaling. Indeed, STING−/− spleens had greater numbers of donor Nur77GFP CD4+ T cells expressing GFP, CD69 and IFNγ 6 days post-HSCT (Fig. D). Overall, these data provide a mechanism by which STING could promote CD8+ T cell-mediated GVHD yet diminish CD4+-mediated GVHD. Ongoing studies are exploring the use of STING agonists to augment GVL following aHSCT.
Gene regulatory networks associated with lateral root and nodule development in soybean

Abstract Legume plants such as soybean produce two major types of root lateral organs, lateral roots and root nodules. A robust computational framework was developed to predict potential gene regulatory networks (GRNs) associated with root lateral organ development in soybean. A genome-scale expression dataset was obtained from soybean root nodules and lateral roots and subjected to biclustering using QUBIC. Biclusters (BCs) and transcription factor (TF) genes with enriched expression in lateral root tissues were converged using different network inference algorithms to predict high confident regulatory modules that are repeatedly retrieved in different methods. The ranked combination of results from all different network inference algorithms into one ensemble solution identified 21 GRN modules of 182 co-regulated genes networks potentially involved in root lateral organ development stages in soybean. The pipeline correctly predicted previously known nodule- and LR-associated TFs including the expected hierarchical relationships. The results revealed high scorer AP2, GRF5, and C3H co-regulated GRN modules during early nodule development; and GRAS, LBD41, and ARR18 co-regulated GRN modules late during nodule maturation. Knowledge from this work supported by experimental validation in the future is expected to help determine key gene targets for biotechnological strategies to optimize nodule formation and enhance nitrogen fixation.
Definitions of state variables and state space for brain-computer interface Part 2. Extraction and classification of feature vectors

The hypothesis is proposed that the central dynamics of the action-perception cycle has five steps: emergence from an existing macroscopic brain state of a pattern that predicts a future goal state; selection of a mesoscopic frame for action control; execution of a limb trajectory by microscopic spike activity; modification of microscopic cortical spike activity by sensory inputs; construction of mesoscopic perceptual patterns; and integration of a new macroscopic brain state. The basis is the circular causality between microscopic entities (neurons) and the mesoscopic and macroscopic entities (populations) self-organized by axosynaptic interactions. Self-organization of neural activity is bidirectional in all cortices. Upwardly the organization of mesoscopic percepts from microscopic spike input predominates in primary sensory areas. Downwardly the organization of spike outputs that direct specific limb movements is by mesoscopic fields constituting plans to achieve predicted goals. The mesoscopic fields in sensory and motor cortices emerge as frames within macroscopic activity. Part 1 describes the action-perception cycle and its derivative reflex arc qualitatively. Part 2 describes the perceptual limb of the arc from microscopic MSA to mesoscopic wave packets, and from these to macroscopic EEG and global ECoG fields that express experience-dependent knowledge in successive states. These macroscopic states are conceived to embed and control mesoscopic frames in premotor and motor cortices that are observed in local ECoG and LFP of frontoparietal areas. The fields sampled by ECoG and LFP are conceived as local patterns of neural activity in which trajectories of multiple spike activities (MSA) emerge that control limb movements. Mesoscopic frames are located by use of the analytic signal from the Hilbert transform after band pass filtering. The state variables in frames are measured to construct feature vectors by which to describe and classify frame patterns. Evidence is cited to justify use of linear analysis. The aim of the review is to enable researchers to conceive and identify goal-oriented states in brain activity for use as commands, in order to relegate the details of execution to adaptive control devices outside the brain.
Entrepreneurship as a solution to poverty in developed economies

Abstract While entrepreneurship in developing economies at the base of the pyramid is receiving growing attention, scholars have devoted less effort to exploring entrepreneurship as a solution to poverty in advanced economies. Yet, poverty rates have not meaningfully changed in most developed economies in 50 years, and the income gap between rich and poor continues to widen. In this article, we examine entrepreneurship as a source of empowerment for the economically disadvantaged. We explore the nature of poverty and its implications for various aspects of entrepreneurship, identify problematic aspects of the typical low-income startup, and present the SPODER conceptual framework for fostering entrepreneurial development among the poor: (S) supportive infrastructure, (P) preparation of the entrepreneur; (O) expanded opportunity horizons; (D) finding sources of differentiation; (E) a well-designed economic model; and (R) leveraging community resources. We conclude by drawing from the framework implications for those involved in breaking the cycle of poverty.
The influence of relationship closeness on default-mode network connectivity during social interactions

Reciprocated trust plays a critical role in forming and maintaining relationships, and has consistently been shown to implicate neural circuits involved in reward-related processing and social cognition. Less is known about neural network connectivity during social interactions involving trust, however, particularly as a function of closeness between an investor and a trustee. We examined network reactivity and connectivity in participants who played an economic trust game with close friends, strangers and a computer. Network reactivity analyses showed enhanced activation of the default-mode network (DMN) to social relative to non-social outcomes. A novel network psychophysiological interaction (nPPI) analysis revealed enhanced connectivity between the DMN and the superior frontal gyrus and superior parietal lobule when experiencing reciprocated vs violated trust from friends relative to strangers. Such connectivity tracked with differences in self-reported social closeness with these partners. Interestingly, reactivity of the executive control network (ECN), involved in decision processes, demonstrated no social vs non-social preference, and ECN-ventral striatum connectivity did not track social closeness. Taken together, these novel findings suggest that DMN interacts with components of attention and control networks to signal the relative importance of positive experiences with close others vs strangers.
Partitioning a graph into small pieces with applications to path transversal

Given a graph $$G = (V, E)$$ and an integer $$k \in \mathbb {N}$$, we study $$k$$-Vertex Separator (resp. $$k$$-Edge Separator), where the goal is to remove the minimum number of vertices (resp. edges) such that each connected component in the resulting graph has less than k vertices. We also study $$k$$-Path Transversal, where the goal is to remove the minimum number of vertices such that there is no simple path of length k. Our main results are the following improved approximation algorithms.
Speaker Diarization Using Latent Space Clustering in Generative Adversarial Network

In this work, we propose deep latent space clustering for speaker diarization using generative adversarial network (GAN) back-projection with the help of an encoder network. The proposed diarization system is trained jointly with GAN loss, latent variable recovery loss, and a clustering-specific loss. It uses x-vector speaker embeddings at the input, while the latent variables are sampled from a combination of continuous random variables and discrete one-hot encoded variables using the original speaker labels. We benchmark our proposed system on the AMI meeting corpus, and two child-clinician interaction corpora (ADOS and BOSCC) from the autism diagnosis domain. ADOS and BOSCC contain diagnostic and treatment outcome sessions respectively obtained in clinical settings for verbal children and adolescents with autism. Experimental results show that our proposed system significantly outperform the state-of-the-art x-vector based diarization system on these databases. Further, we perform embedding fusion with x-vectors to achieve a relative diarization error rate (DER) improvement of 31%, 36% and 49% on AMI eval, ADOS and BOSCC corpora respectively, when compared to the x-vector baseline using oracle speech segmentation.
Towards Fast and Accurate Streaming End-To-End ASR

End-to-end (E2E) models fold the acoustic, pronunciation and language models of a conventional speech recognition model into one neural network with a much smaller number of parameters than a conventional ASR system, thus making it suitable for on-device applications. For example, recurrent neural network transducer (RNN-T) as a streaming E2E model has shown promising potential for on-device ASR [1]. For such applications, quality and latency are two critical factors. We propose to reduce E2E model’s latency by extending the RNN-T endpointer (RNN-T EP) model [2] with additional early and late penalties. By further applying the minimum word error rate (MWER) training technique [3], we achieved 8.0% relative word error rate (WER) reduction and 130ms 90-percentile latency reduction over [2] on a Voice Search test set. We also experimented with a second-pass Listen, Attend and Spell (LAS) rescorer [4]. Although it did not directly improve the first pass latency, the large WER reduction provides extra room to trade WER for latency. RNN-T EP+LAS, together with MWER training brings in 18.7% relative WER reduction and 160ms 90-percentile latency reductions compared to the original proposed RNN-T EP [2] model.
Uncovering differential identifiability in network properties of human brain functional connectomes.

The Identifiability Framework (If) has been shown to improve differential identifiability (reliability across-sessions and -sites, and differentiability across-subjects) of functional connectomes for a variety of fMRI tasks. But having a robust single session/subject functional connectome is just the starting point to subsequently assess network properties for characterizing properties of integration, segregation and communicability, among others. Naturally, one wonders if uncovering identifiability at the connectome level also uncovers identifiability on the derived network properties. This also raises the question of where to apply the If framework: on the connectivity data or directly on each network measurement? Our work answers these questions by exploring the differential identifiability profiles of network measures when If is applied on 1) the functional connectomes, and 2) directly on derived network measurements.Results show that improving across-session reliability of FCs also improves reliability of derived network measures. We also find that, for specific network properties, application of If directly on network properties is more effective. Finally, we discover that applying the framework, either way, increases task sensitivity of network properties. At a time when the neuroscientific community is moving away from group-average statistics towards subject-level inferences, we have shown that If is a useful tool to enhance robustness in FC fingerprints, which permeates to derived network properties as well.
Modular Curves and Mordell-Weil Torsion in F-theory

In this work we prove a bound for the torsion in Mordell-Weil groups of smooth elliptically fibered Calabi-Yau 3- and 4-folds. In particular, we show that the set of torsion groups which can occur on a smooth elliptic Calabi-Yau n-fold is contained in the set of subgroups which appear on a rational elliptic surface if n ≥ 3 and is slightly larger for n = 2. The key idea in our proof is showing that any elliptic fibration with sufficiently large torsion1 has singularities in codimension 2 which do not admit a crepant resolution. We prove this by explicitly constructing and studying maps to a modular curve whose existence is predicted by a universal property. We use the geometry of modular curves to explain the minimal singularities that appear on an elliptic fibration with prescribed torsion, and to determine the degree of the fundamental line bundle (hence the Kodaira dimension) of the universal elliptic surface which we show to be consistent with explicit Weierstrass models. The constraints from the modular curves are used to bound the fundamental group of any gauge group G in a supergravity theory obtained from F-theory. We comment on the isolated 8-dimensional theories, obtained from extremal K3’s, that are able to circumvent lower dimensional bounds. These theories neither have a heterotic dual, nor can they be compactified to lower dimensional minimal SUGRA theories. We also comment on the maximal, discrete gauged symmetries obtained from certain Calabi-Yau threefold quotients.
Direct numerical simulation of spiral turbulence

In this paper, we present results of three-dimensional direct numerical simulations of the spiral turbulence phenomenon in a range of moderate Reynolds numbers, in which alternating intertwined helical bands of turbulent and laminar fluids co-exist and propagate between two counter-rotating concentric cylinders. We show that the turbulent spiral is comprised of numerous small-scale azimuthally elongated vortices, which align into and collectively form the barber-pole-like pattern. The domain occupied by such vortices in a plane normal to the cylinder axis resembles a ‘crescent moon’, a shape made well known by Van Atta with his experiments in the 1960s. The time-averaged mean velocity of spiral turbulence is characterized in the radial–axial plane by two layers of axial flows of opposite directions. We also observe that, as the Reynolds number increases, the transition from spiral turbulence to featureless turbulence does not occur simultaneously in the whole domain, but progresses in succession from the inner cylinder towards the outer cylinder. Certain aspects pertaining to the dynamics and statistics of spiral turbulence and issues pertaining to the simulation are discussed.
Future Rent-Seeking and Current Public Savings

Abstract The conventional wisdom is that politicians' rent-seeking motives increase public debt and deficits. This is because myopic politicians face political risk and prefer to extract political rents as early as possible. In this paper we study the determination of government debt and deficits in a dynamic political economy model. We show that this conventional wisdom relies on economic volatility being low relative to political uncertainty. If economic volatility is high relative to political uncertainty, then a rent-seeking government actually over-saves and over-taxes along the equilibrium path relative to a benevolent government. This result emerges because of the option value of rent-seeking: a rent-seeking government over-values future funds because of the possibility of using them for future rents instead of cutting taxes in the event of a future boom (when marginal utility of private consumption is low). This over-saving bias is temporary since, in the long run, the rent-seeking government over-borrows relative to the benevolent government as it eventually squanders the funds it has accumulated. We find that both the under-saving and over-saving bias of the government can be solved by a rule of capping deficits.
How Market Sentiment Drives Forecasts of Stock Returns

We reveal a novel channel through which market participants’ sentiment influences how they forecast stock returns: their optimism (pessimism) affects the weights they assign to fundamentals. Our an...
Creep Deformation Mechanisms and Related Microstucture Development of AZ31 Magnesium Alloy

Because of ever increasing demanded of Magnesium alloys in various industries, high temperature deformation of Mg-Al-Zn alloys (AZ31) at constant stress (i.e. creep) were studied at a wide range of stresses and temperatures to characterize underlying deformation mechanism and dynamic recrystallization (DRX) Various microstructures (e.g. grain growth & DRX) are noted during steady-state creep mechanisms such as grain boundary sliding (GBS), dislocation glide creep (DGC) and dislocation climb creep (DCC). Although a combination of DRX and grain growth is characteristic of low stacking fault energy materials like Mg alloys at elevated temperatures, observation reveals grain growth at low strain-rates (GBS region) along with dynamic recovery (DRV) mechanism. Scanning Electron Microscopic (SEM) characterization of the fracture surface reveals more inter-granular fracture for large grains (i.e. GBS region with DRV process) and more dimple shape fracture for small grains (i.e. DGC & DCC region with DRX).
Chapter 5 – Special Random Variables

This chapter discusses certain types of random variables that occur over and over again in applications. The expectation of a Bernoulli random variable is the probability that the random variable equals 1. The random variables described are the Poisson probability distribution, the hypergeometric random variable, the uniform random variable, normal random variable, exponential random variable, the gamma distribution, and the distributions arising from the normal and local distribution. To determine the mean and variance of a Poisson random variable, its moment generating function need to be determined first. The Poisson random variable has a wide range of applications in a variety of areas. The uniform distribution arises in practice when a certain random variable is supposed to be equally likely to be near any value in the interval. The chapter also provides several examples and practice exercise.
Effects of core turbulence on jet excitability

The effects of varying freestream core turbulence on the evolution of a circular jet with and without tonal excitation are examined. Measurements are made on an 8.8 cm diameter jet at a Mach number of 0.3. The jet is excited by plane waves at Strouhal number 0.5. For the excited and unexcited cases the turbulence level is varied by screens and grids placed upstream of the nozzle exit. The experiment results are compared with a theoretical model which incorporates a variable core turbulence and considers the energy interactions between the mean flow, the turbulence and the forced component. Both data and theory indicate that increasing the freestream turbulence diminishes the excitability of the jet and reduces the effect of excitation on the spreading rate of the jet.
Lasers, Solid-state

An overview of solid-state lasers, which are lasers using doped, dielectric solids as the gain media, is presented. The article includes information on the history, materials physics, technology, and application of these devices. Keywords: solid-state lasers; fiber lasers; rare-earth-doped materials; transition-metal-doped materials