cssi/SciSciGPT-SciSciNet huggingface dataset:
Changes in Vegetation Cover of Yellowstone National Park Estimated from MODIS Greenness Trends, 2000 to 2018

Trends and transitions in the MODerate resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series at 250-m resolution were analyzed for the period from 2000 to 2018 to understand recent patterns of vegetation change in Yellowstone National Park (USA). Statistical change in the NDVI time series was detected using the “Breaks for Additive Seasonal and Trend” (BFAST) method. This structural change analysis showed that around 30% of abrupt negative shifts in NDVI over the years 2000 to 2018 could be explained by the impacts of recent wildfires. At least one breakpoint could be detected at 12% of the 250-m MODIS pixel locations within the entire YNP study area since the year 2000, but that the majority (about 70%) of NDVI breakpoints detected in vegetation greenness could be not be explained by the impacts of recent wildfires. Positive growing season NDVI trends were detected across most young forests and regrowing (from recent fire disturbance) woodland cover. Evidence further suggested that the 1988 wildfire burns did not pre-dispose vegetation cover in YNP to a higher number of abrupt negative shifts in NDVI since the year 2000. The wildfires of 1988 were associated with significantly higher NDVI recovery trends over the recent 18-year MODIS time series, compared to areas unburned in 1988. Locations on the Northern Range that showed the highest greening trends since 2000 were commonly located in sagebrush steppe-dominated vegetation communities growing at lower than 2500 m elevation. Results from NDVI trend analysis supported the hypothesis that years with relatively high snowpack water content, such as 2007–2008 and 2010–2011, were most closely associated with abrupt negative shifts in NDVI, but no findings strongly supported the supposition that vegetation cover in YNP is changing in greenness in association solely with warming in surface air temperatures or with extreme drought periods across the study region over the past two decades.
Approximate capacity of the two-way relay channel: A deterministic approach

We study the capacity of the full-duplex bidirectional (or two-way) relay channel with two nodes and one relay. The channels in the forward direction are assumed to be different (in general) than the channels in the backward direction, i.e. channel reciprocity is not assumed. We use the recently proposed deterministic approach to capture the essence of the problem and to determine a good transmission and relay strategy for the Gaussian channel. Depending on the ratio of the individual channel gains, we propose to use either a simple amplify-and-forward or a particular superposition coding strategy at the relay. We analyze the achievable rate region and show that the scheme achieves to within 3 bits the cut-set bound for all values of channel gains.
Adaptive low-complexity sequential inference for Dirichlet process mixture models

We develop a sequential low-complexity inference procedure for Dirichlet process mixtures of Gaussians for online clustering and parameter estimation when the number of clusters are unknown a-priori. We present an easily computable, closed form parametric expression for the conditional likelihood, in which hyper-parameters are recursively updated as a function of the streaming data assuming conjugate priors. Motivated by large-sample asymptotics, we propose a novel adaptive low-complexity design for the Dirichlet process concentration parameter and show that the number of classes grow at most at a logarithmic rate. We further prove that in the large-sample limit, the conditional likelihood and data predictive distribution become asymptotically Gaussian. We demonstrate through experiments on synthetic and real data sets that our approach is superior to other online state-of-the-art methods.
Learning Lexico-Functional Patterns for First-Person Affect

Informal first-person narratives are a unique resource for computational models of everyday events and people’s affective reactions to them. People blogging about their day tend not to explicitly say I am happy. Instead they describe situations from which other humans can readily infer their affective reactions. However current sentiment dictionaries are missing much of the information needed to make similar inferences. We build on recent work that models affect in terms of lexical predicate functions and affect on the predicate’s arguments. We present a method to learn proxies for these functions from first-person narratives. We construct a novel fine-grained test set, and show that the patterns we learn improve our ability to predict first-person affective reactions to everyday events, from a Stanford sentiment baseline of .67F to .75F.
Scheduling Post-Disaster Repairs in Electricity Distribution Networks

Natural disasters, such as hurricanes, earthquakes, and large wind or ice storms, typically cause damages to a large number of components in electricity distribution networks. Since power cannot be restored until these damages are repaired, strategically scheduling the repairs by available crews could reduce the harm done to the affected community. Considering the radial structure of many distribution networks, we model this repair and restoration process as a scheduling problem with soft precedence constraints. As a benchmark, we first formulate this problem as a time-indexed integer linear program (LP) with valid inequalities. Three approximation algorithms with performance guarantees are then proposed to solve this problem: first, an LP-based list scheduling algorithm, second, a single to multi-crew repair schedule conversion algorithm, and third, a dispatch rule based on $\rho$ -factors which can be interpreted as component importance measures. Numerical results validate the effectiveness of the proposed methods.
How Well Do Doctors Know Their Patients? Evidence from a Mandatory Access Prescription Drug Monitoring Program

Many opioid control policies target the prescribing behavior of health care providers. In this paper, we study the first comprehensive state-level policy requiring providers to access patients' opioid history before making prescribing decisions. We compare prescribers in Kentucky, which implemented this policy in 2012, to those in a control state, Indiana. Our main difference-in-differences analysis uses the universe of prescriptions filled in the two states to assess how the information provided affected prescribing behavior. We find that a significant share of low-volume providers stopped prescribing opioids altogether after the policy was implemented, though this change accounted for a small share of the reduction in total volume. The most important margin of response was to prescribe opioids to fewer patients. Although providers disproportionately discontinued treating patients whose opioid histories showed the use of multiple providers, there were also economically meaningful reductions for patients without multiple providers and single-use acute patients.
Lcuts: Linear Clustering of Bacteria Using Recursive Graph Cuts

Bacterial biofilm segmentation poses significant challenges due to lack of apparent structure, poor imaging resolution, limited contrast between conterminous cells and high density of cells that overlap. Although there exist bacterial segmentation algorithms in the existing art, they fail to delineate cells in dense biofilms, especially in 3D imaging scenarios in which the cells are growing and subdividing in a complex manner. A graph-based data clustering method, ${\mathcal{L}}{\text{Cuts}}$, is presented with the application on bacterial cell segmentation. By constructing a weighted graph with node features in locations and principal orientations, the proposed method can automatically classify and detect differently oriented aggregations of linear structures (represent by bacteria in the application). The method assists in the assessment of several facets, such as bacterium tracking, cluster growth, and mapping of migration patterns of bacterial biofilms. Quantitative and qualitative measures for 2D data demonstrate the superiority of proposed method over the state of the art. Preliminary 3D results exhibit reliable classification of the cells with 97% accuracy.
CRISPR-Cas9 Gene Editing in Lizards through Microinjection of Unfertilized Oocytes

CRISPR-Cas9-mediated gene editing has enabled the direct manipulation of gene function in many species. However, the reproductive biology of reptiles presents unique barriers for the use of this technology, and there are no reptiles with effective methods for targeted mutagenesis. Here, we demonstrate that the microinjection of immature oocytes within the ovaries of Anolis sagrei females enables the production of CRISPR-Cas9-induced mutations. This method is capable of producing F0 embryos and hatchlings with monoallelic or biallelic mutations. We demonstrate that these mutations can be transmitted through the germline to establish genetically modified strains of lizards. Direct tests of gene function can now be performed in Anolis lizards, an important model for studies of reptile evolution and development.
Modeling Color Terminology Across Thousands of Languages

There is an extensive history of scholarship into what constitutes a “basic” color term, as well as a broadly attested acquisition sequence of basic color terms across many languages, as articulated in the seminal work of Berlin and Kay (1969). This paper employs a set of diverse measures on massively cross-linguistic data to operationalize and critique the Berlin and Kay color term hypotheses. Collectively, the 14 empirically-grounded computational linguistic metrics we design—as well as their aggregation—correlate strongly with both the Berlin and Kay basic/secondary color term partition (γ = 0.96) and their hypothesized universal acquisition sequence. The measures and result provide further empirical evidence from computational linguistics in support of their claims, as well as additional nuance: they suggest treating the partition as a spectrum instead of a dichotomy.
Fast Enhanced CT Metal Artifact Reduction Using Data Domain Deep Learning

Filtered back projection (FBP) is the most widely used method for image reconstruction in X-ray computed tomography (CT) scanners, and can produce excellent images in many cases. However, the presence of dense materials, such as metals, can strongly attenuate or even completely block X-rays, producing severe streaking artifacts in the FBP reconstruction. These metal artifacts can greatly limit subsequent object delineation and information extraction from the images, restricting their diagnostic value. This problem is particularly acute in the security domain, where there is great heterogeneity in the objects that can appear in a scene, highly accurate decisions must be made quickly, and processing time is highly constrained. The standard practical approaches to reducing metal artifacts in CT imagery are either simplistic nonadaptive interpolation-based projection data completion methods or direct image post-processing methods. These standard approaches have had limited success. Motivated primarily by security applications, we present a new deep-learning-based metal artifact reduction approach that tackles the problem in the projection data domain. We treat the projection data corresponding to dense, metal objects as missing data and train an adversarial deep network to complete the missing data directly in the projection domain. The subsequent complete projection data is then used with conventional FBP to reconstruct an image intended to be free of artifacts. This new approach results in an end-to-end metal artifact reduction algorithm that is computationally efficient textcolorredand therefore practical and fits well into existing CT workflows allowing easy adoption in existing scanners. Training deep networks can be challenging, and another contribution of our work is to demonstrate that training data generated using an accurate X-ray simulation can be used to successfully train the deep network, when combined with transfer learning using limited real data sets. We demonstrate the effectiveness and potential of our algorithm on simulated and real examples.
The molecular determinants of R-roscovitine block of hERG channels.

Human ether-a-go-go-related gene (Kv11.1, or hERG) is a potassium channel that conducts the delayed rectifier potassium current (IKr) during the repolarization phase of cardiac action potentials. hERG channels have a larger pore than other K+channels and can trap many unintended drugs, often resulting in acquired LQTS (aLQTS). R-roscovitine is a cyclin-dependent kinase (CDK) inhibitor that induces apoptosis in colorectal, breast, prostate, multiple myeloma, other cancer cell lines, and tumor xenografts, in micromolar concentrations. It is well tolerated in phase II clinical trials. R-roscovitine inhibits open hERG channels but does not become trapped in the pore. Two-electrode voltage clamp recordings from Xenopus oocytes expressing wild-type (WT) or hERG pore mutant channels (T623A, S624A, Y652A, F656A) demonstrated that compared to WT hERG, T623A, Y652A, and F656A inhibition by 200 μM R-roscovitine was ~ 48%, 29%, and 73% weaker, respectively. In contrast, S624A hERG was inhibited more potently than WT hERG, with a ~ 34% stronger inhibition. These findings were further supported by the IC50 values, which were increased for T623A, Y652A and F656A (by ~5.5, 2.75, and 42 fold respectively) and reduced 1.3 fold for the S624A mutant. Our data suggest that while T623, Y652, and F656 are critical for R-roscovitine-mediated inhibition, S624 may not be. Docking studies further support our findings. Thus, R-roscovitine's relatively unique features, coupled with its tolerance in clinical trials, could guide future drug screens.
MONET: Multiview Semi-Supervised Keypoint Detection via Epipolar Divergence

This paper presents MONET---an end-to-end semi-supervised learning framework for a keypoint detector using multiview image streams. In particular, we consider general subjects such as non-human species where attaining a large scale annotated dataset is challenging. While multiview geometry can be used to self-supervise the unlabeled data, integrating the geometry into learning a keypoint detector is challenging due to representation mismatch. We address this mismatch by formulating a new differentiable representation of the epipolar constraint called epipolar divergence---a generalized distance from the epipolar lines to the corresponding keypoint distribution. Epipolar divergence characterizes when two view keypoint distributions produce zero reprojection error. We design a twin network that minimizes the epipolar divergence through stereo rectification that can significantly alleviate computational complexity and sampling aliasing in training. We demonstrate that our framework can localize customized keypoints of diverse species, e.g., humans, dogs, and monkeys.
Accounting for fixed assets and investment efficiency: a real options framework

In a perfect world, a manager's investment in fixed assets would increase with the assets’ profitability. However, when managers privately know their project profitability and care about their comp...
Deep Log-Likelihood Ratio Quantization

In this work, a deep learning-based method for log-likelihood ratio (LLR) lossy compression and quantization is proposed, with emphasis on a single-input single-output uncorrelated fading communication setting. A deep autoencoder network is trained to compress, quantize and reconstruct the bit log-likelihood ratios corresponding to a single transmitted symbol. Specifically, the encoder maps to a latent space with dimension equal to the number of sufficient statistics required to recover the inputs — equal to three in this case — while the decoder aims to reconstruct a noisy version of the latent representation with the purpose of modeling quantization effects in a differentiable way. Simulation results show that, when applied to a standard rate-1/2 low-density parity-check (LDPC) code, a finite precision compression factor of nearly three times is achieved when storing an entire codeword, with an incurred loss of performance lower than 0.15 dB compared to straightforward scalar quantization of the log-likelihood ratios and the method is competitive with state-of-the-art approaches.
Molecular Pathway Analysis Indicates a Distinct Metabolic Phenotype in Women With Right-Sided Colon Cancer

Colon cancer is the third most commonly diagnosed cancer in the United States. Recent reports have shown that the location of the primary tumor is of clinical importance. Patients with right-sided colon cancers (RCCs) (tumors arising between the cecum and proximal transverse colon) have poorer clinical outcomes than those with left-sided colon cancers (LCCs) (tumors arising between the distal transverse colon and sigmoid colon, excluding the rectum). Interestingly, women have a lower incidence of colon cancer than men, but have a higher propensity for RCC. The reason for this difference is not known; however, identification of sex-specific differences in gene expression by tumor anatomical location in the colon could provide further insight. Moreover, it could reveal important predictive markers for response to various treatments. This study provides a comprehensive bioinformatic analysis of various genes and molecular pathways that correlated with sex and anatomical location of colon cancers using four publicly available annotated data sets housed in the National Center for Biotechnology Information's Gene Expression Omnibus. We identified differentially expressed genes in tumor tissues from women with RCC, which showed attenuated energy and nutrient metabolism when compared with women with LCC. Specifically, we showed the downregulation of 5' AMP-activated protein kinase alpha subunit (AMPKα) and anti-tumor immune responses in women with RCC. This difference was not seen when comparing tumor tissues from men with RCC to men with LCC. Therefore, women with RCC may have a specific metabolic and immune phenotype which accounts for differences in prognosis and treatment response.
On Pattern-Avoiding Fishburn Permutations

The class of permutations that avoid the bivincular pattern $$(231, \{1\},\{1\})$$ is known to be enumerated by the Fishburn numbers. In this paper, we call them Fishburn permutations and study their pattern avoidance. For classical patterns of size 3, we give a complete enumerative picture for regular and indecomposable Fishburn permutations. For patterns of size 4, we focus on a Wilf equivalence class of Fishburn permutations that are enumerated by the Catalan numbers. In addition, we also discuss a class enumerated by the binomial transform of the Catalan numbers and give conjectures for other equivalence classes of pattern-avoiding Fishburn permutations.
Comparison of visceral adipose tissue DNA methylation and gene expression profiles in female adolescents with obesity.

Epigenetic changes in visceral adipose tissue (VAT) with obesity and their effects on gene expression are poorly understood, especially during emergent obesity in youth. The current study tested the hypothesis that methylation and gene expression profiles of key growth factor and inflammatory pathways are altered in VAT from obese compared to non-obese youth. VAT samples from adolescent females grouped as Lean (L; n = 15; age = 15 ± 3 years, BMI = 21.9 ± 3.0 kg/m2) or Obese (Ob; n = 15, age = 16 ± 2 years, BMI = 45.8 ± 9.8 kg/m2) were collected. Global methylation (n = 20) and gene expression (N = 30) patterns were profiled via microarray and interrogated for differences between groups by ANCOVA (p < 0.05), followed by biological pathway analyses. Overlapping differences in methylation and gene expression in 317 genes were found in VAT from obese compared to lean groups. PI3K/AKT Signaling (p = 1.83 × 10−6; 11/121 molecules in dataset/pathway) was significantly overrepresented in Ob VAT according to pathway analysis. Upregulations in the PI3K/AKT signaling pathway mRNAs TFAM (p = 0.03; fold change = 1.8) and PPP2R5C (p = 0.03, FC = 2.6) were confirmed via qRT-PCR. Our analyses show obesity-related differences in DNA methylation and gene expression in visceral adipose tissue of adolescent females. Specifically, we identified methylation site/gene expression pairs differentially regulated and mapped these differences to pathways including PI3K/AKT signaling, suggesting that PI3K/AKT signaling pathway dysfunction in obesity may be driven in part by changes in DNA methylation.
Conceptual Aspects of Global Value Chains

The paper offers an overview of some key conceptual aspects associated with the rise of global value chains (GVCs). It outlines a series of alternative interpretations and definitions of what the rise of GVCs entails, and it traces the implications of these alternative conceptualizations for the measurement of the phenomenon, as well as for elucidating the key determinants and implications of GVC participation, both at the country level and at the firm level. In the process, the paper offers some speculative thoughts about the future of GVCs in light of the advent of an array of new technologies.
Single-cell ATAC-Seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell-specific type 2 diabetes regulatory signatures.

Abstract Objective Type 2 diabetes (T2D) is a complex disease characterized by pancreatic islet dysfunction, insulin resistance, and disruption of blood glucose levels. Genome-wide association studies (GWAS) have identified > 400 independent signals that encode genetic predisposition. More than 90% of associated single-nucleotide polymorphisms (SNPs) localize to non-coding regions and are enriched in chromatin-defined islet enhancer elements, indicating a strong transcriptional regulatory component to disease susceptibility. Pancreatic islets are a mixture of cell types that express distinct hormonal programs, so each cell type may contribute differentially to the underlying regulatory processes that modulate T2D-associated transcriptional circuits. Existing chromatin profiling methods such as ATAC-seq and DNase-seq, applied to islets in bulk, produce aggregate profiles that mask important cellular and regulatory heterogeneity. Methods We present genome-wide single-cell chromatin accessibility profiles in >1,600 cells derived from a human pancreatic islet sample using single-cell combinatorial indexing ATAC-seq (sci-ATAC-seq). We also developed a deep learning model based on U-Net architecture to accurately predict open chromatin peak calls in rare cell populations. Results We show that sci-ATAC-seq profiles allow us to deconvolve alpha, beta, and delta cell populations and identify cell-type-specific regulatory signatures underlying T2D. Particularly, T2D GWAS SNPs are significantly enriched in beta cell-specific and across cell-type shared islet open chromatin, but not in alpha or delta cell-specific open chromatin. We also demonstrate, using less abundant delta cells, that deep learning models can improve signal recovery and feature reconstruction of rarer cell populations. Finally, we use co-accessibility measures to nominate the cell-specific target genes at 104 non-coding T2D GWAS signals. Conclusions Collectively, we identify the islet cell type of action across genetic signals of T2D predisposition and provide higher-resolution mechanistic insights into genetically encoded risk pathways.
Volume growth, curvature, and Buser-type inequalities in graphs

We study the volume growth of metric balls as a function of the radius in discrete spaces, and focus on the relationship between volume growth and discrete curvature. We improve volume growth bounds under a lower bound on the so-called Ollivier curvature, and discuss similar results under other types of discrete Ricci curvature. Following recent work in the continuous setting of Riemannian manifolds (by the first author), we then bound the eigenvalues of the Laplacian of a graph under bounds on the volume growth. In particular, the spectral gap of the graph can be bounded using a weighted discrete Hardy inequality and the higher eigenvalues of the graph can be bounded by the eigenvalues of a tridiagonal matrix times a multiplicative factor, both of which only depend on the volume growth of the graph. As a direct application, we relate the eigenvalues to the Cheeger isoperimetric constant. Using these methods, we describe classes of graphs for which the Cheeger inequality is tight on the second eigenvalue. We also describe a method for proving Buser's inequality in graphs, particularly under a lower bound assumption on curvature.
A Parallel Implementation of a Fast Multipole-Based 3-D Capacitance Extraction Program on Distributed Memory Multicomputers

Very fast and accurate 3-D capacitance extraction is essential for interconnect optimization in VLSI ultra-deep sub-micron designs (UDSM). Parallel processing provides an approach to reducing the simulation turn-around time. This paper examines the parallelization of the well-known fast multipole-based 3-D capacitance extraction program FASTCAP, which employs new adaptive and preconditioning techniques. To account for the complicated data dependencies in the unstructured problems, we propose a novel generalized cost function model, which can be used to accurately measure the workload associated with each cube in the hierarchy. We then present two adaptive partitioning schemes, combined with efficient communication mechanisms with bounded buffer size, to reduce the parallel processing overhead. The overall load balance is achieved through balancing the load at each level of the multipole computation. We report detailed performance results on a variety of distributed memory parallel platforms, using standard benchmarks on 3-D capacitance extraction.
The Cannabis Proteome Draft Map Project

Recently we have seen a relaxation of the historic restrictions on the use and subsequent research on the Cannabis plants, generally classified as Cannabis sativa and Cannabis indica. What research has been performed to date has centered on chemical analysis of plant flower products, namely cannabinoids and various terpenes that directly contribute to phenotypic characteristics of the female flowers. In addition, we have seen many groups recently completing genetic profiles of various plants of commercial value. To date, no comprehensive attempt has been made to profile the proteomes of these plants. We report herein our progress on constructing a comprehensive draft map of the Cannabis proteome. To date we have identified over 17,000 potential protein sequences. Unfortunately, no annotated genome of Cannabis plants currently exists. We present a method by which “next generation” DNA sequencing output and shotgun proteomics data can be combined to produce annotated FASTA files, bypassing the need for annotated genetic information altogether in traditional proteomics workflows. The resulting material represents the first comprehensive annotated protein FASTA for any Cannabis plant. Using this annotated database as reference we can refine our protein identifications, resulting in the confident identification of 13,000 proteins with putative function. Furthermore, we demonstrate that post-translational modifications play an important role in the proteomes of Cannabis flower, particularly lysine acetylation and protein glycosylation. To facilitate the evolution of analytical investigations into these plant materials, we have created a portal to host resources developed from our proteomic and metabolomic analysis of Cannabis plant material as well as our results integrating these resources.
Disorder and critical current variability in Josephson junctions

We investigate theoretically the origins of observed variations in the critical currents of Nb/Al-AlO x/Nb Josephson junctions in terms of various types of disorder. We consider the following disorder sources: vacancies within the Al layer, thickness variations in the AlO x layer, and “pinholes” (i.e., point contacts) within the AlO x layer. The calculations are all performed by solving the microscopic Bogoliubov–de Gennes Hamiltonian self-consistently. It is found that a small concentration of vacancies within the Al layer is sufficient to suppress the critical current, while the presence of a small number of thick regions of the oxide layer induces a similar effect as well. The pinhole scenario is found to result in anomalous behavior that resembles neither that of a pure tunnel junction nor that of a superconductor–normal–superconductor junction but a regime that interpolates between these two limits. We comment on the degree to which each of the three scenarios describes the actual situation present in these junctions.
Frontal theta and beta oscillations during lower-limb movement in Parkinson’s disease

Abstract Objectives Patients with Parkinson’s disease (PD) have deficits in lower-limb functions such as gait, which involves both cognitive and motor dysfunction. In PD, theta and beta brain rhythms are associated with cognitive and motor functions, respectively. We tested the hypothesis that PD patients with lower-limb abnormalities would exhibit abnormal theta and beta rhythms in the mid-frontal cortical region during lower-limb action. Methods This study included thirty-nine participants; 13 PD patients with FOG (PDFOG+), 13 without FOG (PDFOG-), and 13 demographically-matched controls. We recorded scalp electroencephalograms (EEG) during a lower-limb pedaling motor task, which required intentional initiation and stopping of a motor movement. Results FOG scores were correlated with disease severity and cognition. PDFOG+ patients pedaled with reduced speed and decreased acceleration compared to PDFOG− patients and controls. PDFOG+ patients exhibited attenuated theta-band (4–8 Hz) power and increased beta-band (13–30 Hz) power at mid-frontal electrode Cz during pedaling. Frontal theta- and beta-band oscillations also correlated with motor and cognitive deficits. Conclusion Frontal theta and beta oscillations are predictors of lower-limb motor symptoms in PD and could be used to design neuromodulation for PD-related lower-limb abnormalities. Significance These data provide insight into mechanisms of lower-limb dysfunction in PD with FOG.
Learning improves decoding of odor identity with phase-referenced oscillations in the olfactory bulb.

Local field potential oscillations reflect temporally coordinated neuronal ensembles-coupling distant brain regions, gating processing windows, and providing a reference for spike timing-based codes. In phase amplitude coupling (PAC), the amplitude of the envelope of a faster oscillation is larger within a phase window of a slower carrier wave. Here, we characterized PAC, and the related theta phase-referenced high gamma and beta power (PRP), in the olfactory bulb of mice learning to discriminate odorants. PAC changes throughout learning, and odorant-elicited changes in PRP increase for rewarded and decrease for unrewarded odorants. Contextual odorant identity (is the odorant rewarded?) can be decoded from peak PRP in animals proficient in odorant discrimination, but not in naive mice. As the animal learns to discriminate the odorants the dimensionality of PRP decreases. Therefore, modulation of phase-referenced chunking of information in the course of learning plays a role in early sensory processing in olfaction.
An investigation of Newton-Sketch and subsampled Newton methods

Sketching, a dimensionality reduction technique, has received much attention in the statistics community. In this paper, we study sketching in the context of Newton's method for solving finite-sum ...
SketchyCoreSVD: SketchySVD from Random Subsampling of the Data Matrix

We present a method called SketchyCoreSVD to compute the near-optimal rank r SVD of a data matrix by building random sketches only from its subsampled columns and rows. We provide theoretical guarantees under incoherence assumptions, and validate the performance of our SketchyCoreSVD method on various large static and time-varying datasets.
The Fukaya category of the pillowcase, traceless character varieties, and Khovanov cohomology

For a diagram of a 2-stranded tangle in the 3-ball we define a twisted complex of compact Lagrangians in the triangulated envelope of the Fukaya category of the smooth locus of the pillowcase. We show that this twisted complex is a functorial invariant of the isotopy class of the tangle, and that it provides a factorization of Bar-Natan's functor from the tangle cobordism category to chain complexes. In particular, the hom set of our invariant with a particular non-compact Lagrangian associated to the trivial tangle is naturally isomorphic to the reduced Khovanov chain complex of the closure of the tangle. Our construction comes from the geometry of traceless SU(2) character varieties associated to resolutions of the tangle diagram, and was inspired by Kronheimer and Mrowka's singular instanton link homology.
UAV Access Point Placement for Connectivity to a User with Unknown Location Using Deep RL

In recent years, unmanned aerial vehicles (UAVs) have been considered for telecommunications purposes as relays, caches, or IoT data collectors. In addition to being easy to deploy, their maneuverability allows them to adjust their location to optimize the capacity of the link to the user equipment on the ground or of the link to the basestation. The majority of the previous work that analyzes the optimal placement of such a UAV makes at least one of two assumptions: the channel can be predicted using a simple model or the locations of the users on the ground are known. In this paper, we use deep reinforcement learning (deep RL) to optimally place a UAV serving a ground user in an urban environment, without the previous knowledge of the channel or user location. Our algorithm relies on signal-to-interference-plus- noise ratio (SINR) measurements and a 3D map of the topology to account for blockage and scatterers. Furthermore, it is designed to operate in any urban environment. Results in conditions simulated by a ray tracing software show that with the constraint on the maximum number of iterations our algorithm has a 90% success rate in converging to a target SINR.
Distribution Network Marginal Costs: Enhanced AC OPF Including Transformer Degradation

This paper considers the day-ahead operational planning problem of radial distribution networks hosting Distributed Energy Resources, such as Solar Photovoltaic (PV) and Electric Vehicles (EVs). We present an enhanced AC Optimal Power Flow (OPF) model that estimates dynamic Distribution nodal Location Marginal Costs (DLMCs) encompassing transformer degradation as a short-run network variable cost. We decompose real/reactive power DLMCs into additive marginal cost components representing respectively the costs of real/reactive power transactions at the T&D interface, real/reactive power marginal losses, voltage and ampacity congestion, and transformer degradation, capturing intertemporal coupling. Decomposition is useful in identifying sources of marginal costs and facilitating the employment of distributed AC OPF algorithms. DLMCs convey sufficient information to represent the benefit of shifting real/reactive power across time and achieve optimal Distribution Network and DER operation. We present and analyze actual distribution feeder based numerical results involving a wealth of future EV/PV adoption scenarios and illustrate the superiority of our approach relative to reasonable conventional scheduling alternatives. Overwhelming evidence from extensive numerical results supports the significant benefits of internalizing short-run marginal asset—primarily transformer—degradation.
Sparse identification of contrast gain control in the fruit fly photoreceptor and amacrine cell layer

The fruit fly’s natural visual environment is often characterized by light intensities ranging across several orders of magnitude and by rapidly varying contrast across space and time. Fruit fly photoreceptors robustly transduce and, in conjunction with amacrine cells, process visual scenes and provide the resulting signal to downstream targets. Here, we model the first step of visual processing in the photoreceptor-amacrine cell layer. We propose a novel divisive normalization processor (DNP) for modeling the computation taking place in the photoreceptor-amacrine cell layer. The DNP explicitly models the photoreceptor feedforward and temporal feedback processing paths and the spatio-temporal feedback path of the amacrine cells. We then formally characterize the contrast gain control of the DNP and provide sparse identification algorithms that can efficiently identify each the feedforward and feedback DNP components. The algorithms presented here are the first demonstration of tractable and robust identification of the components of a divisive normalization processor. The sparse identification algorithms can be readily employed in experimental settings, and their effectiveness is demonstrated with several examples.
A microfluidic platform for functional testing of cancer drugs on intact tumor slices

Present approaches to assess cancer treatments are often inaccurate, costly, and/or cumbersome. Functional testing platforms that use live tumor cells are a promising tool both for drug development and for identifying the optimal therapy for a given patient, i.e. precision oncology. However, current methods that utilize patient-derived cells from dissociated tissue typically lack the microenvironment of the tumor tissue and/or cannot inform on a timescale rapid enough to guide decisions for patient-specific therapy. We have developed a microfluidic platform that allows for multiplexed drug testing of intact tumor slices cultured on a porous membrane. The device is digitally-manufactured in a biocompatible thermoplastic by laser-cutting and solvent bonding. Here we describe the fabrication process in detail, we characterize the fluidic performance of the device, and demonstrate on-device drug-response testing with tumor slices from xenografts and from a patient colorectal tumor.
Searches for Lorentz-Violating Signals with Astrophysical Polarization Measurements

Astrophysical observations are a powerful tool to constrain effects of Lorentz-invariance violation in the photon sector. Objects at high redshifts provide the longest possible baselines, and gamma-ray telescopes allow us to observe some of the highest energy photons. Observations include polarization measurements and time-of-flight measurements of transient or variable objects to constrain vacuum birefringence and dispersion. Observing multiple sources covering the entire sky allows the extraction of constraints on anisotropy. In this paper, I review methods and recent results on Lorentz- and CPT-invariance violation constraints derived from astrophysical polarization measurements in the framework of the Standard-Model Extension.
Adaptive Optimal Control of Linear Periodic Systems: An Off-Policy Value Iteration Approach

This article studies the infinite-horizon adaptive optimal control of continuous-time linear periodic (CTLP) systems. A novel value iteration (VI) based off-policy adaptive dynamic programming (ADP) algorithm is proposed for a general class of CTLP systems, so that approximate optimal solutions can be obtained directly from the collected data, without the exact knowledge of system dynamics. Under mild conditions, the proofs on uniform convergence of the proposed algorithm to the optimal solutions are given for both the model-based and model-free cases. The VI-based ADP algorithm is able to find suboptimal controllers without assuming the knowledge of an initial stabilizing controller. Application to the optimal control of a triple inverted pendulum subjected to a periodically varying load demonstrates the feasibility and effectiveness of the proposed method.
2 – Rabies Virus

Publisher Summary Rabies virus (RABV) is the prototype virus of the genus Lyssavirus in the family Rhabdoviridae. RABV has a similar morphology, chemical structure and life cycle to vesicular stomatitis virus, the prototype virus of the genus Vesiculovirus, in the same family. RABV and the variants known as the rabies-related lyssaviruses, which share with rabies virus the unique capability to produce a rabies-like encephalomyelitis, along with other RABV characteristics, belong to one of seven lyssavirus genotypes. Lyssaviruses share many of the biological and physicochemical features that are associated with other rhabdoviruses. These lyssavirus proteins share many of the biological functions that the same viral proteins have in other rhabdoviruses. Lyssaviruses, like other rhabdoviruses, also use similar mechanisms to enter susceptible cells, express and replicate their genome RNA and release mature virus particles from the plasma membrane of infected cells. This chapter focuses on the rabies virus structure, molecular composition and morphology, the structure and organization of the RNA genome, and the molecular biology of the rabies virus proteins. The chapter also reviews the current knowledge of the virus' life cycle in infected host cells and mechanisms of virus spread.
Multiple Baseline Designs

Multiple-baseline designs are a type of single-case design used to study treatment effects across multiple participants, multiple behaviors, or multiple settings. Baselines are established by repeated observation. Interventions are then implemented at different times for the different participants, behaviors, or settings. Effects are demonstrated when changes are observed that coincide with the interventions. Keywords: single-case design; interrupted time series design; reversal design
Social Security and Retirement Decisions

One of the most striking features of the postwar U.S. economy has been the rapid decrease in the labor force participation of the elderly at a time when the health of this group has been improving. In spite of this, previous research, based on retrospective interviews with the retired population, usually concludes that poor health accounts for the overwhelming majority of retirements. This paper suggests that nothing could be further from the truth. Using data from the Panel Study of Income Dynamics, we follow a cohort of white married males through their sixties to estimate a model of retirement behavior. Using several definitions of retirement suggested in the literature, we find that the two key policy parameters of the social security system—the income guarantee and the implicit tax on earnings—exert an enormous influence on retirement decisions. For example, our results suggest that a decrease in the implicit tax rate on earnings from one-half to one-third would reduce the annual probability of retirement by about fifty percent! Applying the coefficient estimates to time series data on the labor force participation of the elderly implies that the social security system has been the major factor in the explosion in earlier retirement.
Nuclear electric propulsion /NEP/ spacecraft for the outer planet orbiter mission

The design, operating features, and a possible Neptune orbit for the spacecraft powered by the SP-100 nuclear electric propulsion (NEP) system under study by NASA and the DOE are described. The system features a reactor and a payload situated on opposite ends of a 0.5 m diam, 11 m long astromast. Mercury-ion thrusters are located beneath the reactor for side thrusting, and no contamination of the payload or obstruction of the viewing angles for scientific objectives occurs with the system, which would not degrade in performance even under high insolation during near-sun maneuvers. Results of a theoretical study of earth escapes are presented to show that an NEP powered spiral trajectory out of a 700 km Shuttle orbit and using a Triton gravity assist would be superior to departing from a 300 km orbit with a Centaur boost. The mission profile includes a 1249 kg Galileo payload. The SP-100 has a 1.4 MWth reactor with UO2 fuel tiles and weighs 19,904 kg.
8 – Species Interactions

Publisher Summary This chapter gives an overview of species interactions and describes the major classes of interactions, factors that affect these interactions, and consequences of these interactions for community organization. The species population in a community interacts in many ways that affect community structure and dynamics. Species interactions vary considerably in their form, strength, and effect, and create quite complex networks among species. One species can influence the behavior or abundance of another species directly, e.g., a predator feeding on its prey, or indirectly through effects on other interacting species. The web of interactions, direct and indirect, and having positive or negative feedback, determines the structure and dynamics of the community and controls rates of energy and matter fluxes through ecosystems. The study briefly explains the classes of interactions, factors affecting interactions, and consequences of interactions. In addition, varieties of factors influence the nature and intensity of an interaction. Abiotic factors that affect the activity or condition of individuals of a species alter their competitive, predatory, or defensive ability. Resource availability, particularly the quality and patchiness of resources, mitigates or exacerbates competition or predation by limiting the likelihood that competitors, or predators and their prey, co-occur in time and space.
Challenges in Forecasting Malicious Events from Incomplete Data

The ability to accurately predict cyber-attacks would enable organizations to mitigate their growing threat and avert the financial losses and disruptions they cause. But how predictable are cyber-attacks? Researchers have attempted to combine external data – ranging from vulnerability disclosures to discussions on Twitter and the darkweb – with machine learning algorithms to learn indicators of impending cyber-attacks. However, successful cyber-attacks represent a tiny fraction of all attempted attacks: the vast majority are stopped, or filtered by the security appliances deployed at the target. As we show in this paper, the process of filtering reduces the predictability of cyber-attacks. The small number of attacks that do penetrate the target’s defenses follow a different generative process compared to the whole data which is much harder to learn for predictive models. This could be caused by the fact that the resulting time series also depends on the filtering process in addition to all the different factors that the original time series depended on. We empirically quantify the loss of predictability due to filtering using real-world data from two organizations. Our work identifies the limits to forecasting cyber-attacks from highly filtered data.
Kinetics of vaporization of lead sulfide

The kinetics of vaporization of lead sulfide were investigated in the temperature range 785 to 993 °C using a thermogravimetric balance. The effect of nonreactive ambient atmosphere on the vaporization kinetics was examined in some detail. Under He-rich He-N2 atmosphere the rate of vaporization was found to be about four times as large as that under N2-atmosphere. A mathematical model was developed taking into consideration the chemical kinetic and mass-transfer factors. It was found that faster vaporization rates obtained with He-rich atmosphere are due to the higher diffusivity of PbS(g) in He(g). The chemical kinetic aspects of vaporization appear to be unaffected by the ambient atmosphere. The activation energy for vaporization has a value of 198.95 kJ · mole−1 in the temperature range investigated. The evaporation coefficient was found to have values ranging from 0.05 to 0.14.