cssi/SciSciGPT-SciSciNet huggingface dataset:
Minimax Rates of Estimation for Sparse PCA in High Dimensions

We study sparse principal components analysis in the high-dimensional setting, wherep (the number of variables) can be much larger thann(the number of observations). We prove optimal, non-asymptotic lower and upper bounds on the minimax estimation error for the leading eigenvector when it belongs to an! q ball forq![0,1]. Our bounds are sharp inpandnfor allq![0,1] over a wide class of distributions. The upper bound is obtained by analyzing the performance of! q constrained PCA. In particular, our results provide convergence rates for! 1 -constrained PCA.
A domain-specific analysis system for examining nuclear reactor simulation data for light-water and sodium-cooled fast reactors

Abstract Building a new generation of fission reactors in the United States presents many technical and regulatory challenges. One important challenge is the need to share and present results from new high-fidelity, high-performance simulations in an easily usable way. Since modern multiscale, multi-physics simulations can generate petabytes of data, they will require the development of new techniques and methods to reduce the data to familiar quantities of interest (e.g., pin powers, temperatures) with a more reasonable resolution and size. Furthermore, some of the results from these simulations may be new quantities for which visualization and analysis techniques are not immediately available in the community and need to be developed. This paper describes a new system for managing high-performance simulation results in a domain-specific way that naturally exposes quantities of interest for light water and sodium-cooled fast reactors. It describes requirements to build such a system and the technical challenges faced in its development at all levels (simulation, user interface, etc.). An example comparing results from two different simulation suites for a single assembly in a light-water reactor is presented, along with a detailed discussion of the system’s requirements and design.
The Effects of Career and Technical Education: Evidence from the Connecticut Technical High School System

We examine the effect of attending stand-alone technical high schools on student short- and long-term outcomes using a regression discontinuity design. Male students are 10 percentage points more likely to graduate from high school and have half a semester less time enrolled in college, although effects on college fade-out. Male students have 32% higher quarterly earnings. Earnings effects may in part reflect general skills: male students have higher attendance rates and test scores, and industry fixed effects explain less than 1/3rd of earnings gains. We find little evidence that attending a technical high school affects the outcomes of female students.
A Three-Sample Test for Introgression

Many methods exist for detecting introgression between nonsister species, but the most commonly used require either a single sequence from four or more taxa or multiple sequences from each of three taxa. Here, we present a test for introgression that uses only a single sequence from three taxa. This test, denoted D3, uses similar logic as the standard D-test for introgression, but by using pairwise distances instead of site patterns it is able to detect the same signal of introgression with fewer species. We use simulations to show that D3 has statistical power almost equal to D, demonstrating its use on a data set of wild bananas (Musa). The new test is easy to apply and easy to interpret, and should find wide use among currently available data sets.
Image Classification on IoT Edge Devices: Profiling and Modeling

With the advent of powerful, low-cost IoT systems, processing data closer to where the data originates, known as edge computing, has become an increasingly viable option. In addition to lowering the cost of networking infrastructures, edge computing reduces edge-cloud delay, which is essential for mission-critical applications. In this paper, we show the feasibility and study the performance of image classification using IoT devices. Specifically, we explore the relationships between various factors of image classification algorithms that may affect energy consumption, such as dataset size, image resolution, algorithm type, algorithm phase, and device hardware. In order to provide a means of predicting the energy consumption of an edge device performing image classification, we investigate the usage of three machine learning algorithms using the data generated from our experiments. The performance as well as the trade-offs for using linear regression, Gaussian process, and random forests are discussed and validated. Our results indicate that the random forest model outperforms the two former algorithms, with an R-squared value of 0.95 and 0.79 for two different validation datasets. The random forest also served as a feature extraction mechanism which enabled us to identify which predictor variables influenced our model the most.
Functionalized and Degradable Polyphthalaldehyde Derivatives

Polymers that depolymerize back to monomers can be repeatedly chemically recycled, thereby reducing their environmental impact. Polyphthalaldehyde is a metastable polymer that can rapidly and quant...
Model Asset eXchange: Path to Ubiquitous Deep Learning Deployment

A recent trend observed in traditionally challenging fields such as computer vision and natural language processing has been the significant performance gains shown by deep learning (DL). In many different research fields, DL models have been evolving rapidly and become ubiquitous. Despite researchers' excitement, unfortunately, most software developers are not DL experts and oftentimes have a difficult time following the booming DL research outputs. As a result, it usually takes a significant amount of time for the latest superior DL models to prevail in industry. This issue is further exacerbated by the common use of sundry incompatible DL programming frameworks, such as Tensorflow, PyTorch, Theano, etc. To address this issue, we propose a system, called Model Asset Exchange (MAX), that avails developers of easy access to state-of-the-art DL models. Regardless of the underlying DL programming frameworks, it provides an open source Python library (called the MAX framework) that wraps DL models and unifies programming interfaces with our standardized RESTful APIs. These RESTful APIs enable developers to exploit the wrapped DL models for inference tasks without the need to fully understand different DL programming frameworks. Using MAX, we have wrapped and open-sourced more than 30 state-of-the-art DL models from various research fields, including computer vision, natural language processing and signal processing, etc. In the end, we selectively demonstrate two web applications that are built on top of MAX, as well as the process of adding a DL model to MAX.
Substrate Strain Mitigates Effects of β-Aminopropionitrile-Induced Reduction in Enzymatic Crosslinking

Enzymatic crosslinks stabilize type I collagen and are catalyzed by lysyl oxidase (LOX), a step interrupted through β-aminopropionitrile (BAPN) exposure. This study evaluated dose-dependent effects of BAPN on osteoblast gene expression of type I collagen, LOX, and genes associated with crosslink formation. The second objective was to characterize collagen produced in vitro after exposure to BAPN, and to explore changes to collagen properties under continuous cyclical substrate strain. To evaluate dose-dependent effects, osteoblasts were exposed to a range of BAPN dosages (0–10 mM) for gene expression analysis and cell proliferation. Results showed significant upregulation of BMP-1, POST, and COL1A1 and change in cell proliferation. Results also showed that while the gene encoding LOX was unaffected by BAPN treatment, other genes related to LOX activation and matrix production were upregulated. For the loading study, the combined effects of BAPN and mechanical loading were assessed. Gene expression was quantified, atomic force microscopy was used to extract elastic properties of the collagen matrix, and Fourier Transform infrared spectroscopy was used to assess collagen secondary structure for enzymatic crosslinking analysis. BAPN upregulated BMP-1 in static samples and BAPN combined with mechanical loading downregulated LOX when compared to control-static samples. Results showed a higher indentation modulus in BAPN-loaded samples compared to control-loaded samples. Loading increased the mature-to-immature crosslink ratios in control samples, and BAPN increased the height ratio in static samples. In summary, effects of BAPN (upregulation of genes involved in crosslinking, mature/immature crosslinking ratios, upward trend in collagen elasticity) were mitigated by mechanical loading.
Compulsive exercise or exercise dependence? Clarifying conceptualizations of exercise in the context of eating disorder pathology

Abstract Objective Maladaptive exercise relates to eating disorder (ED) pathology and impairment in clinical and non-clinical populations. At present, two different conceptualizations of maladaptive exercise are often studied in relation to ED pathology: compulsive exercise and exercise dependence. Compulsive exercise functions to avoid negative affect (e.g., guilt and anxiety) associated with not exercising, whereas exercise dependence is associated with tolerance to exercise benefits and avoidance of exercise withdrawal. At present, clinicians and researchers struggle to determine the most appropriate term for describing problematic exercise in individuals with ED pathology. This study aimed to directly compare these conceptualizations of maladaptive exercise in relation to severity of ED pathology. Design This study examined cross-sectional data. Method Undergraduate participants (N = 235, 78% female) with elevated ED pathology completed the Eating Disorder Examination Questionnaire (EDE-Q), Compulsive Exercise Test (CET), and Exercise Dependence Scale (EDS). Multiple linear regression analyses evaluated associations between EDE-Q and CET and EDS scores and dominance analysis determined which qualities of exercise were uniquely associated with EDE-Q scores. Results Results suggest that compulsive qualities of exercise, including exercise to control shape and weight and to avoid negative affect are more strongly associated with severity of ED pathology than qualities of exercise dependence. Conclusions Clinicians and researchers working with ED populations can benefit from prioritizing assessments that capture compulsive qualities of exercise. Additionally, these results suggest that interventions that effectively target other compulsive behaviors (e.g., exposure and response prevention) may be promising treatment options for problematic exercise in the context of EDs.
Temperature-jump solution X-ray scattering reveals distinct motions in a dynamic enzyme

Correlated motions of proteins are critical to function, but these features are difficult to resolve using traditional structure determination techniques. Time-resolved X-ray methods hold promise for addressing this challenge, but have relied on the exploitation of exotic protein photoactivity, and are therefore not generalizable. Temperature jumps, through thermal excitation of the solvent, have been utilized to study protein dynamics using spectroscopic techniques, but their implementation in X-ray scattering experiments has been limited. Here, we perform temperature-jump small- and wide-angle X-ray scattering measurements on a dynamic enzyme, cyclophilin A, demonstrating that these experiments are able to capture functional intramolecular protein dynamics on the microsecond timescale. We show that cyclophilin A displays rich dynamics following a temperature jump, and use the resulting time-resolved signal to assess the kinetics of conformational changes. Two relaxation processes are resolved: a fast process is related to surface loop motions, and a slower process is related to motions in the core of the protein that are critical for catalytic turnover.
Elemental Abundances in M31: First Alpha and Iron Abundance Measurements in M31's Giant Stellar Stream

We present the first measurements of [Fe/H] and [α/Fe] abundances, obtained using spectral synthesis modeling, for red giant branch stars in M31's giant stellar stream (GSS). The spectroscopic observations, obtained at a projected distance of 17 kpc from M31's center, yielded 61 stars with [Fe/H] measurements, including 21 stars with [α/Fe] measurements, from 112 targets identified as M31 stars. The [Fe/H] measurements confirm the expectation from photometric metallicity estimates that stars in this region of M31's halo are relatively metal rich compared to stars in the Milky Way's inner halo: more than half the stars in the field, including those not associated with kinematically identified substructure, have [Fe/H] abundances >-1.0. The stars in this field are α-enhanced at lower metallicities, while [α/Fe] decreases with increasing [Fe/H] above metallicities of [Fe/H] ≳ −0.9. Three kinematical components have been previously identified in this field: the GSS, a second kinematically cold feature of unknown origin, and M31's kinematically hot halo. We compare probabilistic [Fe/H] and [α/Fe] distribution functions for each of the components. The GSS and the second kinematically cold feature have very similar abundance distributions, while the halo component is more metal poor. Although the current sample sizes are small, a comparison of the abundances of stars in the GSS field with abundances of M31 halo and dSph stars from the literature indicate that the progenitor of the stream was likely more massive, and experienced a higher efficiency of star formation, than M31's existing dSphs or the dEs NGC 147 and NGC 185.
Rational buyers search when prices increase

Abstract We develop a dynamic pricing model motivated by observed patterns in business-to-business (and some business-to-customer) transactions. Seller costs are perfectly correlated and evolve according to a Markov process. In every period, each buyer observes (for free) the price set by their current supplier, but not the other sellers' prices or the sellers' (common) cost level. By paying a cost s the buyer becomes “active” and benefits from (Bertrand) competition among sellers. We show that there exists a semi-separating equilibrium whereby sellers increase price immediately when costs increase and otherwise decrease price gradually. Moreover, buyers become active when prices increase but not otherwise. In sum, we deliver a theory whereby buyers become active (“search”) if and only if their supplier increases price.
LXRβ controls glioblastoma cell growth, lipid balance, and immune modulation independently of ABCA1

Cholesterol is a critical component of membranes and a precursor for hormones and other signaling molecules. Previously, we showed that unlike astrocytes, glioblastoma cells do not downregulate cholesterol synthesis when plated at high density. In this report, we show that high cell density induces ABCA1 expression in glioblastoma cells, enabling them to get rid of excess cholesterol generated by an activated cholesterol biosynthesis pathway. Because oxysterols are agonists for Liver X Receptors (LXRs), we investigated whether increased cholesterol activates LXRs to maintain cholesterol homeostasis in highly-dense glioblastoma cells. We observed that dense cells had increased oxysterols, which activated LXRβ to upregulate ABCA1. Cells with CRISPR-mediated knockdown of LXRβ, but not ABCA1, had decreased cell cycle progression and cell survival, and decreased feedback repression of the mevalonate pathway in densely-plated glioma cells. LXRβ gene expression poorly correlates with ABCA1 in glioblastoma patients, and expression of each gene correlates with poor patient prognosis in different prognostic subtypes. Finally, gene expression and lipidomics analyses cells revealed that LXRβ regulates the expression of immune response gene sets and lipids known to be involved in immune modulation. Thus, therapeutic targeting of LXRβ in glioblastoma might be effective through diverse mechanisms.
Understanding financial auditing from a service perspective

Abstract This paper draws from the service science and professional service literatures to conceptualize financial auditing as an economic service. A central characteristic of economic services is the participation of the customer/client in the production process. The necessity of having the customer/client be a co-producer introduces greater heterogeneity to the provision of services relative to the manufacturing of goods which, in turn, creates a tension between service quality and service efficiency. One implication of this tension is that standardization of the audit process may not increase audit quality. We further argue that audit research should give more attention to the idiosyncratic nature of audit engagements and the importance of successful cooperation between the service provider (the audit firm) and the client for improving audit quality. Utilizing research on service networks, we draw attention to a broader perspective than the dyadic relations of service provider and client to show that the possible frictions between the value of co-creation of the service and the independence of the service professional are endemic to the service process, implying that efforts to maximize auditor independence may have unexpected costs that impair audit quality.
Reliability-based voxel selection

While functional magnetic resonance imaging (fMRI) studies typically measure responses across the whole brain, not all regions are likely to be informative for a given study. Which voxels should be considered? Here we propose a method for voxel selection based on the reliability of the data. This method isolates voxels that respond consistently across imaging runs while maximizing the reliability of multi-voxel patterns across the selected voxels. We estimate that it is suitable for designs with at least 15 conditions. In two example datasets, we found that this proposed method defines a smaller set of voxels than another common method, activity-based voxel selection. Broadly, this method eliminates the need to define regions or statistical thresholds a priori and puts the focus on data reliability as the first step in analyzing fMRI data.
Array Codes With Local Properties

In general, array codes consist of $m\times n$ arrays and in many cases, the arrays satisfy parity constraints along lines of different slopes (generally with a toroidal topology). Such codes are useful for RAID type of architectures, since they allow to replace finite field operations by XORs. We present expansions to traditional array codes of this type, like Blaum-Roth (BR) and extended EVENODD codes, by adding parity on columns. This vertical parity allows for recovery of one or more symbols in a column locally, i.e., by using the remaining symbols in the column without invoking the rest of the array. Properties and applications of the new codes are discussed, in particular to Locally Recoverable (LRC) codes.
Selective Mortality and Malnutrition in India

India presents itself as a paradox with low infant mortality and high malnutrition. This paper provides survival bias as an explanation of the paradox. Using pooled health surveys from 1993 to 2005 and a pseudo-panel selection model, this study finds that the change in Height-for-Age Z-Scores (HAZ scores) can be explained by mortality selection. Specifically, children with sample average characteristics that survive have 17.4% less HAZ scores than a child randomly drawn from the population indicating an overestimation of malnutrition in India. This is consistent with the hypothesis of weaker children surviving due to skilled delivery which pulls down the overall HAZ scores. The results are robust to controls for unobservable characteristics of groups of women. Son preference is also apparent in the results. The selection is more evident among male children and in the states where sex selection is historically seen as a problem in India.
Microbial biogeography and ecology of the mouth and implications for periodontal diseases

In humans, the composition of microbial communities differs among body sites and between habitats within a single site. Patterns of variation in the distribution of organisms across time and space are referred to as "biogeography." The human oral cavity is a critical observatory for exploring microbial biogeography because it is spatially structured, easily accessible, and its microbiota has been linked to the promotion of both health and disease. The biogeographic features of microbial communities residing in spatially distinct, but ecologically similar, environments on the human body, including the subgingival crevice, have not yet been adequately explored. The purpose of this paper is twofold. First, we seek to provide the dental community with a primer on biogeographic theory, highlighting its relevance to the study of the human oral cavity. We summarize what is known about the biogeographic variation of dental caries and periodontitis and postulate that disease occurrence reflects spatial patterning in the composition and structure of oral microbial communities. Second, we present a number of methods that investigators can use to test specific hypotheses using biogeographic theory. To anchor our discussion, we apply each method to a case study and examine the spatial variation of the human subgingival microbiota in 2 individuals. Our case study suggests that the composition of subgingival communities may conform to an anterior-to-posterior gradient within the oral cavity. The gradient appears to be structured by both deterministic and nondeterministic processes, although additional work is needed to confirm these findings. A better understanding of biogeographic patterns and processes will lead to improved efficacy of dental interventions targeting the oral microbiota.
Deglaciation of the Greenland and Laurentide ice sheets interrupted by glacier advance during abrupt coolings

Abstract Understanding patterns of ice-sheet deglaciation is key for predicting the rate of future ice-sheet melt, yet the processes underlying deglaciation remain elusive. The early Holocene (11.7 ka to 8.2 ka; Greenlandian Stage) represents the most recent period when the Laurentide and Greenland ice sheets underwent large-scale recession. Moreover, this ice-sheet recession occurred under the backdrop of regional temperatures that were similar to or warmer than today, and comparable to those projected for the upcoming centuries. Reconstructing Laurentide and Greenland ice sheet behavior during the early Holocene, and elucidating the mechanisms dictating this behavior may serve as a partial analog for future Greenland ice-sheet change in a warming world. Here, we present 123 new 10Be surface exposure ages from two sites on Baffin Island and southwestern Greenland that constrain the behavior of the Laurentide and Greenland ice sheets, and an independent alpine glacier during the early Holocene. On Baffin Island, we focus on a unique area where moraines deposited by the Laurentide Ice Sheet rest directly adjacent to moraines deposited by an independent alpine glacier. Sixty-one 10Be ages reveal that advances and/or stillstands of the Laurentide Ice Sheet and an alpine glacier occurred in unison around 11.8 ka, 10.3 ka, and 9.2 ka. Sixty-two 10Be ages from southwestern Greenland indicate that the Greenland Ice Sheet margin experienced re-advances or stillstands around 11.6 ka, 10.4 ka, 9.1 ka, 8.1 ka, and 7.3 ka. Our results reveal that ice sheets respond to climate perturbations on the same centennial timescale as small alpine glaciers. We hypothesize that during the warming climate of the early Holocene, freshening of the North Atlantic Ocean induced by ice-sheet melt resulted in regional cooling and brief periods of ice-sheet stabilization superimposed on net glacier recession. These observations point to a negative feedback mechanism inherent to melting ice sheets in the Baffin Bay region that slows ice-sheet recession during intervals of otherwise rapid deglaciation.
Green's function coupled cluster simulation of the near-valence ionizations of DNA-fragments.

Accurate description of the ionization process in DNA is crucial to the understanding of the DNA damage under exposure to ionizing radiation and the exploration of the potential application of DNA strands in nanoelectronics. In this work, by employing our recently developed Green’s function coupled-cluster library on supercomputing facilities, we have studied the spectral functions of several guanine–cytosine (G–C) base pair structures ([G–C]n, n = 1–3) for the first time in a relatively broad near-valence regime ([−25.0, −5.0] eV) in the coupled-cluster with singles and doubles level. Our focus is to give a preliminary many-body coupled-cluster understanding and guideline of the vertical ionization energy (VIE), spectral profile, and ionization feature changes of these systems as the system size expands in this near-valence regime. The results show that, as the system size expands, even though the lowest VIEs keep decreasing, the changes of spectral function profile and the relative peak positions get unexpectedly smaller. Further analysis of the ionized states associated with the most intensive peak in the spectral functions reveals non-negligible |2h, 1p⟩’s in the ionized wave functions of the considered G–C base pair systems. The leading |2h, 1p⟩’s associated with the main ionizations from the cytosine part of the G–C base pairs feature a transition from the intra-base-pair cytosine π → π* excitation to the inter-base-pair electron excitation as the size of G–C base pairs expands, which also indicates the minimum quantum region in the many-body calculations of DNA systems.
Synthesizing Action Sequences for Modifying Model Decisions.

When a model makes a consequential decision, e.g., denying someone a loan, it needs to additionally generate actionable, realistic feedback on what the person can do to favorably change the decision. We cast this problem through the lens of program synthesis, in which our goal is to synthesize an optimal (realistically cheapest or simplest) sequence of actions that if a person executes successfully can change their classification. We present a novel and general approach that combines search-based program synthesis and test-time adversarial attacks to construct action sequences over a domain-specific set of actions. We demonstrate the effectiveness of our approach on a number of deep neural networks.
Low-Dose Perampanel Rescues Cortical Gamma Dysregulation Associated With Parvalbumin Interneuron GluA2 Upregulation in Epileptic Syngap1+/- Mice.

Abstract Background Loss-of-function SYNGAP1 mutations cause a neurodevelopmental disorder characterized by intellectual disability and epilepsy. SYNGAP1 is a Ras GTPase-activating protein that underlies the formation and experience-dependent regulation of postsynaptic densities. The mechanisms that contribute to this proposed monogenic cause of intellectual disability and epilepsy remain unresolved. Methods We established the phenotype of the epileptogenesis in a Syngap1+/− mouse model using 24-hour video electroencephalography (vEEG)/electromyography recordings at advancing ages. We administered an acute low dose of perampanel, a Food and Drug Administration–approved AMPA receptor (AMPAR) antagonist, during a follow-on 24-hour vEEG to investigate the role of AMPARs in Syngap1 haploinsufficiency. Immunohistochemistry was performed to determine the region- and location-specific differences in the expression of the GluA2 AMPAR subunit. Results A progressive worsening of the epilepsy with emergence of multiple seizure phenotypes, interictal spike frequency, sleep dysfunction, and hyperactivity was identified in Syngap1+/− mice. Interictal spikes emerged predominantly during non–rapid eye movement sleep in 24-hour vEEG of Syngap1+/− mice. Myoclonic seizures occurred at behavioral-state transitions both in Syngap1+/− mice and during an overnight EEG from a child with SYNGAP1 haploinsufficiency. In Syngap1+/− mice, EEG spectral power analyses identified a significant loss of gamma power modulation during behavioral-state transitions. A significant region-specific increase of GluA2 AMPAR subunit expression in the somas of parvalbumin-positive interneurons was identified. Conclusions Acute dosing with perampanel significantly rescued behavioral state–dependent cortical gamma homeostasis, identifying a novel mechanism implicating Ca2+-impermeable AMPARs on parvalbumin-positive interneurons underlying circuit dysfunction in SYNGAP1 haploinsufficiency.
Circuit Mechanisms Underlying Chromatic Encoding in Drosophila Photoreceptors.

Summary Spectral information is commonly processed in the brain through generation of antagonistic responses to different wavelengths. In many species, these color opponent signals arise as early as photoreceptor terminals. Here, we measure the spectral tuning of photoreceptors in Drosophila. In addition to a previously described pathway comparing wavelengths at each point in space, we find a horizontal-cell-mediated pathway similar to that found in mammals. This pathway enables additional spectral comparisons through lateral inhibition, expanding the range of chromatic encoding in the fly. Together, these two pathways enable efficient decorrelation and dimensionality reduction of photoreceptor signals while retaining maximal chromatic information. A biologically constrained model accounts for our findings and predicts a spatio-chromatic receptive field for fly photoreceptor outputs, with a color opponent center and broadband surround. This dual mechanism combines motifs of both an insect-specific visual circuit and an evolutionarily convergent circuit architecture, endowing flies with the ability to extract chromatic information at distinct spatial resolutions.
Functional measurement error in functional regression

Measurement error is an important problem that has not been studied very well in the context of functional data analysis. To the best of our knowledge, there are no existing methods that address the presence of functional measurement errors in generalized functional linear models. In this article, a novel approach is proposed to estimate the slope function in the presence of measurement error in the generalized functional linear model with a scalar response. This work significantly advances the existing conditional score method to accommodate the case where both the measurement error and independent variables lie in infinite dimensional spaces. Asymptotic results are established for the proposed estimate, and its behaviour is studied via simulations, where the response is continuous or binary. Analysis of Canadian Weather data highlights the practical utility of our method. The Canadian Journal of Statistics 48: 238–258; 2020 © 2020 Statistical Society of Canada
Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Time Series

Prediction of future movement of stock prices has been a subject matter of many research work. On one hand, we have proponents of the Efficient Market Hypothesis who claim that stock prices cannot be predicted, on the other hand, there are propositions illustrating that, if appropriately modelled, stock prices can be predicted with a high level of accuracy. There is also a gamut of literature on technical analysis of stock prices where the objective is to identify patterns in stock price movements and profit from it. In this work, we propose a hybrid approach for stock price prediction using machine learning and deep learning-based methods. We select the NIFTY 50 index values of the National Stock Exchange (NSE) of India, over a period of four years: 2015 – 2018. Based on the NIFTY data during 2015 – 2018, we build various predictive models using machine learning approaches, and then use those models to predict the “Close” value of NIFTY 50 for the year 2019, with a forecast horizon of one week, i.e., five days. For predicting the NIFTY index movement patterns, we use a number of classification methods, while for forecasting the actual “Close” values of NIFTY index, various regression models are built. We, then, augment our predictive power of the models by building a deep learning-based regression model using Convolutional Neural Network (CNN) with a walk-forward validation. The CNN model is fine-tuned for its parameters so that the validation loss stabilizes with increasing number of iterations, and the training and validation accuracies converge. We exploit the power of CNN in forecasting the future NIFTY index values using three approaches which differ in number of variables used in forecasting, number of sub-models used in the overall models and, size of the input data for training the models. Extensive results are presented on various metrics for all classification and regression models. The results clearly indicate that CNN-based multivariate forecasting model is the most effective and accurate in predicting the movement of NIFTY index values with a weekly forecast horizon.
Genomic Evidence for Formate Metabolism by Chloroflexi as the Key to Unlocking Deep Carbon in Lost City Microbial Ecosystems

ABSTRACT The Lost City hydrothermal field on the Mid-Atlantic Ridge supports dense microbial life on the lofty calcium carbonate chimney structures. The vent field is fueled by chemical reactions between the ultramafic rock under the chimneys and ambient seawater. These serpentinization reactions provide reducing power (as hydrogen gas) and organic compounds that can serve as microbial food; the most abundant of these are methane and formate. Previous studies have characterized the interior of the chimneys as a single-species biofilm inhabited by the Lost City Methanosarcinales, but they also indicated that this methanogen is unable to metabolize formate. The new metagenomic results presented here indicate that carbon cycling in these Lost City chimney biofilms could depend on the metabolism of formate by Chloroflexi populations. Additionally, we present evidence for metabolically diverse, formate-utilizing Sulfurovum populations and new genomic and phylogenetic insights into the unique Lost City Methanosarcinales. IMPORTANCE Primitive forms of life may have originated around hydrothermal vents at the bottom of the ancient ocean. The Lost City hydrothermal vent field, fueled by just rock and water, provides an analog for not only primitive ecosystems but also potential extraterrestrial rock-powered ecosystems. The microscopic life covering the towering chimney structures at the Lost City has been previously documented, yet little is known about the carbon cycling in this ecosystem. These results provide a better understanding of how carbon from the deep subsurface can fuel rich microbial ecosystems on the seafloor.
Stress Relaxation in Uniquely Oriented SiGe/Si Epitaxial Layers

This study explores stress relaxation of epitaxial SiGe layers grown on Si substrates with unique orientations. The crystallographic orientations of the Si substrates used were off-axis from the (001) plane towards the (111) plane by angles, θ = 0, 10, and 22 degrees. We have grown 100nm thick Si(1-x) Ge(x) epitaxial layers with x=0.3 on the Si substrates to examine the relaxation process. The as-deposited films are metastable to the formation of strain relaxing misfit dislocations, and thermal annealing is used to obtain highly relaxed films for comparison. Raman spectroscopy has been used to measure the strain relaxation, and atomic force microscopy has been used to explore the development of surface morphology. The Raman scattering indicated that the strain in the as-deposited films is dependent on the substrate orientation with strained layers grown on Si with 0 and 22 degree orientations while highly relaxed films were grown on the 10 degree substrate. The surface morphology also differed for the substrate orientations. The 10 degree surface is relatively smooth with hut shaped structures oriented at predicted angles relative to the step edges.
Uniform LSI for the canonical ensemble on the 1D-lattice with strong, finite-range interaction

We consider a one-dimensional lattice system of unbounded, real-valued spins with arbitrary strong, quadratic, finite-range interaction. We show that the canonical ensemble (ce) satisfies a uniform logarithmic Sobolev inequality (LSI). The LSI constant is uniform in the boundary data, the external field and scales optimally in the system size. This extends a classical result of H.T. Yau from discrete to unbounded, real-valued spins. It also extends prior results of Landim et al. or Menz for unbounded, real-valued spins from absent- or weak- to strong-interaction. We deduce the LSI by combining two competing methods, the two-scale approach and the Zegarlinski method. Main ingredients are the strict convexity of the coarse-grained Hamiltonian, the equivalence of ensembles and the decay of correlations in the ce.
The impact of PM2.5 on mortality in older adults: evidence from retirement of coal-fired power plants in the United States.

Evidence of causal relationship between mortality of older adults and low- concentration PM2.5 remains limited. This study investigates the effects of low-concentration PM2.5 on the mortality of adults older than 65 using the closure of coal-fired power plants in the Eastern United States as a natural experiment. We investigated power plants in the Eastern United States (US) that had production changes through unit shutdown or plant retirement between 1999 and 2013. We included only non-clustered power plants without scrubbers and with capacities greater than 50 MW. We used instrumental variable (IV) and difference-in-differences (DID) approaches to estimate the causal impact of PM2.5 concentrations on mortality among Medicare beneficiaries. We compared changes in monthly age-adjusted mortality before and after the retirement of coal-fired plants between the treated and control counties; we accounted for annual wind direction in our selection of treated and control counties. In the models, we initially included only county and monthly fixed effects, and then adjusted for covariates including: 1) only weather variables (temperature, dew point, pressure); and 2) weather variables and socio-economic variables (median household income and poverty rate). The monthly age-adjusted mortality rate averaged across all plants was approximately 423 per 100,000 (SD = 69) and was higher for males than females. Mean PM2.5 concentrations across all counties were 12 μg/m3 (SD = 3.78). Using the IV method, we found that reductions in PM2.5 concentrations significantly decreased monthly mortality among older adults. IV results show that a 1-μg/m3 reduction in PM2.5 concentrations leads to 7.17 fewer deaths per 100,000 per month, or a 1.7% lower monthly mortality rate among people older than 65 years. Using the DID approach, we found that power plant retirement significantly decreased: 1) monthly PM2.5 levels by 2.1 μg/m3, and 2) monthly age-adjusted mortality by approximately 15 people per 100,000 (or 3.6%) in treated counties relative to control counties. The mortality effects were higher among males than females and its impact was the greatest among people older than 75 years. These findings provide evidence of the effectiveness of local, plant-level control measures in reducing near-plant PM2.5 and mortality among U.S. Medicare beneficiaries.
On Secure Network Coding for Multiple Unicast Traffic

This paper investigates the problem of secure communication in a wireline noiseless scenario where a source wishes to communicate to a number of destinations in the presence of a passive external adversary. Different from the multicast scenario, where all destinations are interested in receiving the same message, in this setting different destinations are interested in different messages. The main focus of this paper is on characterizing the secure capacity region, when the adversary has unbounded computational capabilities, but limited network presence. Towards this end, an outer bound on the secure capacity region is derived, and secure transmission schemes are designed and analyzed in terms of achieved rate performance. It is first shown that, for the case of two destinations, the designed scheme matches the outer bound, hence characterizing the secure capacity region. Then, a particular class of networks referred to as two-layer networks is considered, where the source communicates with the destinations by hopping information through one layer of relays. It is shown that the designed scheme is indeed capacity achieving for any two-layer network for which one of the following three conditions is satisfied: (i) the number of destinations is three, (ii) the number of edges eavesdropped by the adversary is one, (iii) the min-cut capacities assume specific values. It is also shown that two-layer networks can be used to model and study a more general class of networks, referred to as separable . The key feature of separable networks is that they can be partitioned into edge disjoint networks that satisfy specific min-cut properties. In particular, it is proved that the secure capacity region of any separable network can be characterized from the secure capacity region of the corresponding two-layer network. Finally, for an arbitrary network topology, a two-phase scheme is designed and its rate performance is compared with the capacity-achieving scheme for networks with two destinations.
The universal fibration with fibre X in rational homotopy theory

Let X be a simply connected space with finite-dimensional rational homotopy groups. Let $$p_\infty :UE \rightarrow B\mathrm {aut}_1(X)$$ be the universal fibration of simply connected spaces with fibre X. We give a DG Lie algebra model for the evaluation map $$ \omega :\mathrm {aut}_1(B\mathrm {aut}_1(X_\mathbb {Q})) \rightarrow B\mathrm {aut}_1(X_\mathbb {Q})$$ expressed in terms of derivations of the relative Sullivan model of $$p_\infty $$. We deduce formulas for the rational Gottlieb group and for the evaluation subgroups of the classifying space $$B\mathrm {aut}_1(X_\mathbb {Q})$$ as a consequence. We also prove that $$\mathbb {C} P^n_\mathbb {Q}$$ cannot be realized as $$B\mathrm {aut}_1(X_\mathbb {Q})$$ for $$n \le 4$$ and X with finite-dimensional rational homotopy groups.
L-Vector: Neural Label Embedding for Domain Adaptation

We propose a novel neural label embedding (NLE) scheme for the domain adaptation of a deep neural network (DNN) acoustic model with unpaired data samples from source and target domains. With NLE method, we distill the knowledge from a powerful source-domain DNN into a dictionary of label embeddings, or l-vectors, one for each senone class. Each l-vector is a representation of the senone-specific output distributions of the source-domain DNN and is learned to minimize the average L 2 , Kullback-Leibler (KL) or symmetric KL distance to the output vectors with the same label through simple averaging or standard back-propagation. During adaptation, the l-vectors serve as the soft targets to train the target-domain model with cross-entropy loss. Without parallel data constraint as in the teacher-student learning, NLE is specially suited for the situation where the paired target-domain data cannot be simulated from the source-domain data. We adapt a 6400 hours multi-conditional US English acoustic model to each of the 9 accented English (80 to 830 hours) and kids’ speech (80 hours). NLE achieves up to 14.1% relative word error rate reduction over direct re-training with one-hot labels.
Generalized bathtub model of network trip flows

Abstract Vickrey (1991, 2020) proposed a bathtub model for the evolution of trip flows served by privately operated vehicles inside a road network based on three premises: (i) treatment of the road network as a single bathtub; (ii) the speed-density relation at the network level, also known as the network fundamental diagram of vehicular traffic, and (iii) the time-independent negative exponential distribution of trip distances. However, the distributions of trip distances are generally time-dependent in the real world, and Vickrey’s model leads to unreasonable results for other types of trip distance distributions. Thus there is a need to develop a bathtub model with more general trip distance distribution patterns. In this study, we present a unified framework for modeling network trip flows with general distributions of trip distances, including negative exponential, constant, and regularly sorting trip distances studied in the literature. In addition to tracking the number of active trips as in Vickrey’s model, this model also tracks the evolution of the distribution of active trips’ remaining distances. We derive four equivalent differential formulations from the network fundamental diagram and the conservation law of trips for the number of active trips with remaining distances not smaller than any value. Then we define and discuss the properties of stationary and gridlock states, derive the integral form of the bathtub model with the characteristic method, and present two numerical methods to solve the bathtub model based on the differential and integral forms respectively. We further study equivalent formulations and solutions for two special types of distributions of trip distances: time-independent negative exponential or deterministic. In particular, we present six equivalent conditions for Vickrey’s bathtub model to be applicable.
5 – Quantifying preconceptions

Chapter 5, Quantifying Preconceptions, argues that we usually know things about the systems we are studying that can be used to supplement actual observations. Temperatures often lie in specific ranges governed by freezing and boiling points. Chemical gradients often vary smoothly in space, owing to the process of diffusion. Energy and momentum obey conservation laws. The methodology through which this prior information can be incorporated into the models is developed in this chapter. Called generalized least squares, it is applied to several substantial examples in which prior information is used to fill in data gaps in datasets.
2 – SOME TOOLS OF THE ECONOMIST

Publisher Summary This chapter discusses tools that help to develop the economic way of thinking. It illustrates the link between capital formation and the future production possibilities of an economy. As more of today's resources are used to produce tools that will make more productive tomorrow, fewer will be available for producing current consumer goods. More investment in machines and human skills will necessitate less current consumption. In most economies, a large number of decisions are made through both the decentralized pricing system and public sector decision making. Both exert considerable influence on to solve fundamental economic problems. Although the two arrangements are different, in each case, the choices of individuals acting as decision makers are important. Economics is about how people make decisions; the tools of economics are applied to both market and public sector action. Constraints on the individual and incentives to pursue various types of activities differ according to whether decisions are made in the public sector or in the marketplace. Changes in personal costs and benefits influence their choices. In turn, the acts of political participants—voters, lobbyists, and politicians—influence public policy and its economic consequences.