cssi/SciSciGPT-SciSciNet huggingface dataset:
The cost of 2-distinguishing hypercubes

Abstract A graph G is said to be 2-distinguishable if there is a labeling of the vertices with two labels so that only the trivial automorphism preserves the labels. The minimum size of a label class, over all 2-distinguishing labelings, is called the cost of 2-distinguishing, denoted by ρ ( G ) . For n ≥ 4 the hypercubes Q n are 2-distinguishable, but the values for ρ ( Q n ) have been elusive, with only bounds and partial results previously known. This paper settles the question. The main result can be summarized as: for n ≥ 4 , ρ ( Q n ) ∈ { 1 + ⌈ log 2 ⁡ n ⌉ , 2 + ⌈ log 2 ⁡ n ⌉ } . Exact values are found using a recursive relationship involving a new parameter ν m , the smallest integer for which ρ ( Q ν m ) = m . The main result is 4 ≤ n ≤ 12 ⟹ ρ ( Q n ) = 5 , and 5 ≤ m ≤ 11 ⟹ ν m = 4 ; for m ≥ 6 , ρ ( Q n ) = m ⇔ 2 m − 2 − ν m − 1 + 1 ≤ n ≤ 2 m − 1 − ν m ; for n ≥ 5 , ν m = n ⇔ 2 n − 1 − ρ ( Q n − 1 ) + 1 ≤ m ≤ 2 n − ρ ( Q n ) .
Second-harmonic generation and the conservation of spatiotemporal orbital angular momentum of light

Light with spatiotemporal orbital angular momentum (ST-OAM) is a recently discovered type of structured and localized electromagnetic field. This field carries characteristic space–time spiral phase structure and transverse intrinsic OAM. Here, we present the generation and characterization of the second harmonic of ST-OAM pulses. We uncover the conservation of transverse OAM in a second-harmonic generation process, where the space–time topological charge of the fundamental field is doubled along with the optical frequency. Our experiment thus suggests a general ST-OAM nonlinear scaling rule, analogous to that in conventional OAM of light. Furthermore, we observe that the topology of a second-harmonic ST-OAM pulse can be modified by complex spatiotemporal astigmatism, giving rise to multiple phase singularities separated in space and time. Our study opens a new route for nonlinear conversion and scaling of light carrying ST-OAM, with the potential for driving other secondary ST-OAM sources of electromagnetic fields and beyond. The second-harmonic spatiotemporal orbital angular momentum of an optical pulse and its space–time topological charge conservation during frequency doubling are experimentally observed, opening opportunities for nonlinear conversion and scaling of photons carrying spatiotemporal orbital angular momentum.
Formal Synthesis of Adaptive Droplet Routing for MEDA Biochips

A digital microfluidic biochip (DMFB) enables the miniaturization of immunoassays, point-of-care clinical diagnostics, and DNA sequencing. A recent generation of DMFBs uses a micro-electrode-dot-array (MEDA) architecture, which provides fine-grained control of droplets and real-time droplet sensing using CMOS technology. However, microelectrodes in a MEDA biochip can degrade due to charge trapping when they are repeatedly charged and discharged during bioassay execution; such degradation leads to the failure of microelectrodes and erroneous bioassay outcomes. To address this problem, we first introduce a new microelectrode-cell design such that we can obtain the health status of all the microelectrodes in a MEDA biochip by employing the inherent sensing mechanism. Next, we present a stochastic game-based model for droplet manipulation, and a formal synthesis method for droplet routing that can dynamically change droplet transportation routes. This adaptation is based on the real-time health information obtained from microelectrodes. Comprehensive simulation results for four real-life bioassays show that our method increases the likelihood of successful bioassay completion with negligible impact on time-to-results.
Prism graphs in tropical plane curves

Any smooth tropical plane curve contains a distinguished trivalent graph called its skeleton. In 2020 Morrison and Tewari proved that the so-called big-face graphs cannot be the skeleta of tropical curves for genus 1 2 and greater. In this paper we answer an open question they posed to extend their result to the prism graphs, proving that a prism graph is the skeleton of a smooth tropical plane curve precisely when the genus is at most 1 1 . Our main tool is a classification of lattice polygons with two points that can simultaneously view all others, without having any one point that can observe all others.
Wolbachia in the spittlebug Prosapia ignipectus: Variable infection frequencies, but no apparent effect on host reproductive isolation.

Animals serve as hosts for complex communities of microorganisms, including endosymbionts that live inside their cells. Wolbachia bacteria are perhaps the most common endosymbionts, manipulating host reproduction to propagate. Many Wolbachia cause cytoplasmic incompatibility (CI), which results in reduced egg hatch when uninfected females mate with infected males. Wolbachia that cause intense CI spread to high and relatively stable frequencies, while strains that cause weak or no CI tend to persist at intermediate, often variable, frequencies. Wolbachia could also contribute to host reproductive isolation (RI), although current support for such contributions is limited to a few systems. To test for Wolbachia frequency variation and effects on host RI, we sampled several local Prosapia ignipectus (Fitch) (Hemiptera: Cercopidae) spittlebug populations in the northeastern United States over two years, including closely juxtaposed Maine populations with different monomorphic color forms, "black" and "lined." We discovered a group-B Wolbachia (wPig) infecting P. ignipectus that diverged from group-A Wolbachia-like model wMel and wRi strains in Drosophila-6 to 46 MYA. Populations of the sister species Prosapia bicincta (Say) from Hawaii and Florida are uninfected, suggesting that P. ignipectus acquired wPig after their initial divergence. wPig frequencies were generally high and variable among sites and between years. While phenotyping wPig effects on host reproduction is not currently feasible, the wPig genome contains three divergent sets of CI loci, consistent with high wPig frequencies. Finally, Maine monomorphic black and monomorphic lined populations of P. ignipectus share both wPig and mtDNA haplotypes, implying no apparent effect of wPig on the maintenance of this morphological contact zone. We hypothesize P. ignipectus acquired wPig horizontally as observed for many Drosophila species, and that significant CI and variable transmission produce high but variable wPig frequencies.
The 3D Structure of Eta Carinae's Nebula: A Definitive Picture from High-Dispersion Near-IR Spectra

High resolution long-slit spectra obtained with the Phoenix spectrograph on Gemini South provide our most accurate probe of the 3D structure of the Homunculus Nebula around Eta Carinae. Emission from molecular hydrogen at 2.122 microns traces a very thin outer skin, which contains the vast majority of the more than 10 solar masses of material in the nebula. This emission, in turn, yields our first definitive picture of the exact shape of the nebula, plus the latitude dependence of the mass-loss rate, speed, kinetic energy, shell thickness, and other properties associated with Eta Car's 19th century explosion. This will be critical for testing any models for the outburst mechanism. A preliminary analysis suggests that explosion from a critically rotating star was the dominant mechanism that gave rise to both the bipolar shape of the nebula and the production of its equatorial disk. [Fe II] emission in the near IR traces a geometrically thicker but less massive shell found on the inner surface of the H2 skin --- this is either a reverse shock that decelerates Eta Car's wind or a warm PDR. [Fe Ill emission also clarifies the structure of an inner "Little Homunculus" seen previously in HST/STlS spectra. Comparing these two tracers of cool molecular gas and warm partially-ionized gas resolves some significant confusion about the complex structure noted in previous studies.
LaneAF: Robust Multi-Lane Detection With Affinity Fields

This study presents an approach to lane detection involving the prediction of binary segmentation masks and per-pixel affinity fields. These affinity fields, along with the binary masks, can then be used to cluster lane pixels horizontally and vertically into corresponding lane instances in a post-processing step. This clustering is achieved through a simple row-by-row decoding process with little overhead; such an approach allows LaneAF to detect a variable number of lanes without assuming a fixed or maximum number of lanes. Moreover, this form of clustering is more interpretable in comparison to previous visual clustering approaches, and can be analyzed to identify and correct sources of error. Qualitative and quantitative results obtained on popular lane detection datasets demonstrate the model's ability to detect and cluster lanes effectively and robustly. Our proposed approach sets a new state-of-the-art on the challenging CULane dataset and the recently introduced Unsupervised LLAMAS dataset.
Decentralized Game-Theoretic Control for Dynamic Task Allocation Problems for Multi-Agent Systems

We propose a decentralized game-theoretic framework for dynamic task allocation problems for multi-agent systems. In our problem formulation, the agents' utilities depend on both the rewards and the costs associated with the successful completion of the tasks assigned to them. The rewards reflect how likely is for the agents to accomplish their assigned tasks whereas the costs reflect the effort needed to complete these tasks (this effort is determined by the solution of corresponding optimal control problems). The task allocation problem considered herein corresponds to a dynamic game whose solution depends on the states of the agents in contrast with classic static (or single-act) game formulations. We propose a greedy solution approach in which the agents negotiate with each other to find a mutually agreeable (or individually rational) task assignment profile based on evaluations of the task utilities that reflect their current states. We illustrate the main ideas of this work by means of extensive numerical simulations.
Effects of the selective dopamine D3 receptor antagonist PG01037 on morphine-induced hyperactivity and antinociception in mice.

Abstract Recent preclinical studies have reported that pretreatment with the novel and highly-selective dopamine D3 receptor (D3R) antagonists R-VK4-40 or VK4-116 attenuates the abuse-related behavioral effects of oxycodone while enhancing its analgesic properties. However, whether these observed effects are generalizable to the broad class of D3R antagonists and/or extend to opioids other than oxycodone has not been extensively explored. The present study sought to assess the impact of pretreatment with another selective D3R antagonist, PG01037, on several behavioral effects of morphine in mice. C57Bl/6 J mice were pretreated with PG01037 (0–10 mg/kg) and tested for 1) hyperlocomotion induced by acute morphine (5.6–56 mg/kg), 2) locomotor sensitization following repeated morphine (56 mg/kg), 3) antinociception following acute morphine (18 mg/kg), and 4) catalepsy following administration of PG01037 alone or in combination with morphine (56 mg/kg). PG01037 dose-dependently attenuated morphine-induced hyperlocomotion and morphine-induced antinociception at doses that did not alter basal locomotion or nociception alone, but did not prevent the induction of locomotor sensitization following repeated morphine administration. Moreover, PG01037 did not induce catalepsy either alone or in combination with morphine. These results suggest that attenuation of acute opioid-induced hyperactivity may be a behavioral effect shared among D3R-selective antagonists, thus supporting continued investigations into their use as potential treatments for opioid use disorder. However, PG01037 is unlike newer, highly-selective D3R antagonists in its capacity to reduce opioid-induced antinociception, indicating that modulation of opioid analgesia may vary across different D3R antagonists.
Multi-Task Hierarchical Learning Based Network Traffic Analytics

Classifying network traffic is the basis for important network applications. Prior research in this area has faced challenges on the availability of representative datasets, and many of the results cannot be readily reproduced. Such a problem is exacerbated by emerging data-driven machine learning based approaches. To address this issue, we present (Net)2 database with three open datasets containing nearly 1.3M labeled flows in total, with a comprehensive list of flow features, for the research community1. We focus on broad aspects in network traffic analysis, including both malware detection and application classification. As we continue to grow them, we expect the datasets to serve as a common ground for AI driven, reproducible research on network flow analytics. We release the datasets publicly and also introduce a Multi-Task Hierarchical Learning (MTHL) model to perform all tasks in a single model. Our results show that MTHL is capable of accurately performing multiple tasks with hierarchical labeling with a dramatic reduction in training time.
UIBert: Learning Generic Multimodal Representations for UI Understanding

To improve the accessibility of smart devices and to simplify their usage, building models which understand user interfaces (UIs) and assist users to complete their tasks is critical. However, unique challenges are proposed by UI-specific characteristics, such as how to effectively leverage multimodal UI features that involve image, text, and structural metadata and how to achieve good performance when high-quality labeled data is unavailable. To address such challenges we introduce UIBert, a transformer-based joint image-text model trained through novel pre-training tasks on large-scale unlabeled UI data to learn generic feature representations for a UI and its components. Our key intuition is that the heterogeneous features in a UI are self-aligned, i.e., the image and text features of UI components, are predictive of each other. We propose five pretraining tasks utilizing this self-alignment among different features of a UI component and across various components in the same UI. We evaluate our method on nine real-world downstream UI tasks where UIBert outperforms strong multimodal baselines by up to 9.26% accuracy.
An ensemble solver for segregated cardiovascular FSI

Computational models are increasingly used for diagnosis and treatment of cardiovascular disease. To provide a quantitative hemodynamic understanding that can be effectively used in the clinic, it is crucial to quantify the variability in the outputs from these models due to multiple sources of uncertainty. To quantify this variability, the analyst invariably needs to generate a large collection of high-fidelity model solutions, typically requiring a substantial computational effort. In this paper, we show how an explicit-in-time ensemble cardiovascular solver offers superior performance with respect to the embarrassingly parallel solution with implicit-in-time algorithms, typical of an inner-outer loop paradigm for non-intrusive uncertainty propagation. We discuss in detail the numerics and efficient distributed implementation of a segregated FSI cardiovascular solver on both CPU and GPU systems, and demonstrate its applicability to idealized and patient-specific cardiovascular models, analyzed under steady and pulsatile flow conditions.
Microglia and Perivascular Macrophages Act as Antigen Presenting Cells to Promote CD8 T Cell Infiltration of the Brain.

CD8 T cell infiltration of the central nervous system (CNS) is necessary for host protection but contributes to neuropathology. Antigen presenting cells (APCs) situated at CNS borders are thought to mediate T cell entry into the parenchyma during neuroinflammation. The identity of the CNS-resident APC that presents antigen via major histocompatibility complex (MHC) class I to CD8 T cells is unknown. Herein, we characterize MHC class I expression in the naive and virally infected brain and identify microglia and macrophages (CNS-myeloid cells) as APCs that upregulate H-2Kb and H-2Db upon infection. Conditional ablation of H-2Kb and H-2Db from CNS-myeloid cells allowed us to determine that antigen presentation via H-2Db, but not H-2Kb, was required for CNS immune infiltration during Theiler's murine encephalomyelitis virus (TMEV) infection and drives brain atrophy as a consequence of infection. These results demonstrate that CNS-myeloid cells are key APCs mediating CD8 T cell brain infiltration.
Training Energy-Efficient Deep Spiking Neural Networks with Single-Spike Hybrid Input Encoding

Spiking Neural Networks (SNNs) have emerged as an attractive alternative to traditional deep learning frameworks, since they provide higher computational efficiency in event driven neuromorphic hardware. However, the state-of-the-art (SOTA) SNNs suffer from high inference latency, resulting from inefficient input encoding and training techniques. The most widely used input coding schemes, such as Poisson based rate-coding, do not leverage the temporal learning capabilities of SNNs. This paper presents a training framework for low-latency energy-efficient SNNs that uses a hybrid encoding scheme at the input layer in which the analog pixel values of an image are directly applied during the first timestep and a novel variant of spike temporal coding is used during subsequent timesteps. In particular, neurons in every hidden layer are restricted to fire at most once per image which increases activation sparsity. To train these hybrid-encoded SNNs, we propose a variant of the gradient descent based spike timing dependent backpropagation (STDB) mechanism using a novel cross entropy loss function based on both the output neurons' spike time and membrane potential. The resulting SNNs have reduced latency and high activation sparsity, yielding significant improvements in computational efficiency. In particular, we evaluate our proposed training scheme on image classification tasks from CIFAR-10 and CIFAR-100 datasets on several VGG architectures. We achieve top-l accuracy of 66.46% with 5 timesteps on the CIFAR-100 dataset with ~125x less compute energy than an equivalent standard ANN. Additionally, our proposed SNN performs 5–300 x faster inference compared to other state-of-the-art rate or temporally coded SNN models.
The corporate general counsel who respects human rights

Global soft law (notably the 2011 UN Guiding Principles on Business and Human Rights ‘UNGPs’), multistakeholder norms, the business practices and policies of leading companies, the expectations of ...
3D Human Pose Estimation with Spatial and Temporal Transformers

Transformer architectures have become the model of choice in natural language processing and are now being introduced into computer vision tasks such as image classification, object detection, and semantic segmentation. However, in the field of human pose estimation, convolutional architectures still remain dominant. In this work, we present PoseFormer, a purely transformer-based approach for 3D human pose estimation in videos without convolutional architectures involved. Inspired by recent developments in vision transformers, we design a spatial-temporal transformer structure to comprehensively model the human joint relations within each frame as well as the temporal correlations across frames, then output an accurate 3D human pose of the center frame. We quantitatively and qualitatively evaluate our method on two popular and standard benchmark datasets: Human3.6M and MPI-INF-3DHP. Extensive experiments show that PoseFormer achieves state-of-the-art performance on both datasets. Code is available at \url{this https URL}
Multiscale and integrative single-cell Hi-C analysis with Higashi.

Single-cell Hi-C (scHi-C) can identify cell-to-cell variability of three-dimensional (3D) chromatin organization, but the sparseness of measured interactions poses an analysis challenge. Here we report Higashi, an algorithm based on hypergraph representation learning that can incorporate the latent correlations among single cells to enhance overall imputation of contact maps. Higashi outperforms existing methods for embedding and imputation of scHi-C data and is able to identify multiscale 3D genome features in single cells, such as compartmentalization and TAD-like domain boundaries, allowing refined delineation of their cell-to-cell variability. Moreover, Higashi can incorporate epigenomic signals jointly profiled in the same cell into the hypergraph representation learning framework, as compared to separate analysis of two modalities, leading to improved embeddings for single-nucleus methyl-3C data. In an scHi-C dataset from human prefrontal cortex, Higashi identifies connections between 3D genome features and cell-type-specific gene regulation. Higashi can also potentially be extended to analyze single-cell multiway chromatin interactions and other multimodal single-cell omics data. Single-cell Hi-C analysis with Higashi identifies cell-to-cell variability of 3D genome organization.
Novel Use of an Internal Distractor for Metacarpal Lengthening.

BackgroundIn patients who have had proximal digit amputation, metacarpal distraction osteogenesis is an option to improve digital length and function. One drawback is that traditional external dist...
On the distribution of twin primes

We introduce a sieve for counting twin primes up to a given range. Our method depends on a parameter ${\lambda}_x$ and the estimation of the number of twin primes obtained as a result, is called a fundamental structure of the distribution of twin primes. Combining the latter with an asymptotic bound of ${\lambda}_x$, establishes venues, conducive to a discovery of a partial result that can be considered as a suitable variant of the prime number theorem. Furthermore, we obtain an asymptotic bound of the number of twin primes less than $x$.
Genome-wide association identifies candidate genes for drought tolerance in coast redwood and giant sequoia.

Drought is a major limitation for survival and growth in plants. With more frequent and severe drought episodes occurring due to climate change, it is imperative to understand the genomic and physiological basis of drought tolerance to be able to predict how species will respond in the future. In this study, univariate and multitrait multivariate GWAS methods were used to identify candidate genes in two iconic and ecosystem-dominating species of the western US - coast redwood and giant sequoia - using ten drought-related physiological and anatomical traits and genome-wide sequence-capture SNPs. Population level phenotypic variation was found in carbon isotope discrimination, osmotic pressure at full turgor, xylem hydraulic diameter and total area of transporting fibers in both species. Our study identified new 78 new marker × trait associations in coast redwood and six in giant sequoia, with genes involved in a range of metabolic, stress and signaling pathways, among other functions. This study contributes to a better understanding of the genomic basis of drought tolerance in long-generation conifers and helps guide current and future conservation efforts in the species.
Neural network features distinguish chemosensory stimuli in Caenorhabditis elegans

Nervous systems extract and process information from the environment to alter animal behavior and physiology. Despite progress in understanding how different stimuli are represented by changes in neuronal activity, less is known about how they affect broader neural network properties. We developed a framework for using graph-theoretic features of neural network activity to predict ecologically relevant stimulus properties, in particular stimulus identity. We used the transparent nematode, Caenorhabditis elegans, with its small nervous system to define neural network features associated with various chemosensory stimuli. We first immobilized animals using a microfluidic device and exposed their noses to chemical stimuli while monitoring changes in neural activity of more than 50 neurons in the head region. We found that graph-theoretic features, which capture patterns of interactions between neurons, are modulated by stimulus identity. Further, we show that a simple machine learning classifier trained using graph-theoretic features alone, or in combination with neural activity features, can accurately predict salt stimulus. Moreover, by focusing on putative causal interactions between neurons, the graph-theoretic features were almost twice as predictive as the neural activity features. These results reveal that stimulus identity modulates the broad, network-level organization of the nervous system, and that graph theory can be used to characterize these changes.
Adversarial Regularization as Stackelberg Game: An Unrolled Optimization Approach

Adversarial regularization has been shown to improve the generalization performance of deep learning models in various natural language processing tasks. Existing works usually formulate the method as a zero-sum game, which is solved by alternating gradient descent/ascent algorithms. Such a formulation treats the adversarial and the defending players equally, which is undesirable because only the defending player contributes to the generalization performance. To address this issue, we propose Stackelberg Adversarial Regularization (SALT), which formulates adversarial regularization as a Stackelberg game. This formulation induces a competition between a leader and a follower, where the follower generates perturbations, and the leader trains the model subject to the perturbations. Different from conventional approaches, in SALT, the leader is in an advantageous position. When the leader moves, it recognizes the strategy of the follower and takes the anticipated follower’s outcomes into consideration. Such a leader’s advantage enables us to improve the model fitting to the unperturbed data. The leader’s strategic information is captured by the Stackelberg gradient, which is obtained using an unrolling algorithm. Our experimental results on a set of machine translation and natural language understanding tasks show that SALT outperforms existing adversarial regularization baselines across all tasks. Our code is publicly available.
A domain decomposition solution of the Stokes-Darcy system in 3D based on boundary integrals

Abstract A framework is developed for a robust and highly accurate numerical solution of the coupled Stokes-Darcy system in three dimensions. The domain decomposition method is based on a Dirichlet-Neumann type splitting of the interface conditions and solving separate Stokes and Darcy problems iteratively. Second kind boundary integral equations are formulated for each problem. The integral equations use a smoothing of the kernels that achieves high accuracy on the boundary, and a straightforward quadrature to discretize the integrals. Numerical results demonstrate the convergence, accuracy, and dependence on parameter values of the iterative solution for a problem of viscous flow around a porous sphere with a known analytical solution, as well as more general surfaces.
Threshold switching in solar cells and a no-scribe photovoltaic technology

We show that thin film cadmium telluride (CdTe) solar cells exhibit the phenomenon of threshold switching similar to that in phase change and resistive memory. It creates a conductive filament (shunt) through the solar cell reaching the buried electrode such as transparent conductive oxide in CdTe based photovoltaics (PV). While in the existing PV, the buried electrode was routinely contacted via laser scribe filled metals, our work paves a way to an alternative technology of no-scribe PV.
Predicting the effects of per- and polyfluoroalkyl substance mixtures on peroxisome proliferator-activated receptor alpha activity in vitro.

Abstract Human exposure to per- and polyfluoroalkyl substances (PFAS) is ubiquitous, with mixtures of PFAS detected in drinking water, food, household dust, and other exposure sources. Animal toxicity studies and human epidemiology indicate that PFAS may act through shared mechanisms including activation of peroxisome proliferator activated receptor α (PPARα). However, the effect of PFAS mixtures on human relevant molecular initiating events remains an important data gap in the PFAS literature. Here, we tested the ability of modeling approaches to predict the effect of diverse PPARα ligands on receptor activity using Cos7 cells transiently transfected with a full length human PPARα (hPPARα) expression construct and a peroxisome proliferator response element-driven luciferase reporter. Cells were treated for 24 hours with two full hPPARα agonists (pemafibrate and GW7647), a full and a partial hPPARα agonist (pemafibrate and mono(2-ethylhexyl) phthalate), or a full hPPARα agonist and a competitive antagonist (pemafibrate and GW6471). Receptor activity was modeled with three additive approaches: effect summation, relative potency factors (RPF), and generalized concentration addition (GCA). While RPF and GCA accurately predicted activity for mixtures of full hPPARα agonists, only GCA predicted activity for full and partial hPPARα agonists and a full agonist and antagonist. We then generated concentration response curves for seven PFAS, which were well-fit with three-parameter Hill functions. The four perfluorinated carboxylic acids (PFCA) tended to act as full hPPARα agonists while the three perfluorinated sulfonic acids (PFSA) tended to act as partial agonists that varied in efficacy between 28-67% of the full agonist, positive control level. GCA and RPF performed equally well at predicting the effects of mixtures with three PFCAs, but only GCA predicted experimental activity with mixtures of PFSAs and a mixture of PFCAs and PFSAs at ratios found in the general population. We conclude that of the three approaches, GCA most accurately models the effect of PFAS mixtures on hPPARα activity in vitro. Understanding the differences in efficacy with which PFAS activate hPPARα is essential for accurately predicting the effects of PFAS mixtures. As PFAS can activate multiple nuclear receptors, future analyses should examine mixtures effects in intact cells where multiple molecular initiating events contribute to proximate effects and functional changes.
Generalizing Correspondence Analysis for Applications in Machine Learning

Correspondence analysis (CA) is a multivariate statistical tool used to visualize and interpret data dependencies by finding maximally correlated embeddings of pairs of random variables. CA has found applications in fields ranging from epidemiology to social sciences; however, current methods do not scale to large, high-dimensional datasets. In this paper, we provide a novel interpretation of CA in terms of an information-theoretic quantity called the principal inertia components. We show that estimating the principal inertia components, which consists in solving a functional optimization problem over the space of finite variance functions of two random variable, is equivalent to performing CA. We then leverage this insight to design novel algorithms to perform CA at an unprecedented scale. Particularly, we demonstrate how the principal inertia components can be reliably approximated from data using deep neural networks. Finally, we show how these maximally correlated embeddings of pairs of random variables in CA further play a central role in several learning problems including visualization of classification boundary and training process, and underlying recent multi-view and multi-modal learning methods.
Regularization by Adversarial Learning for Ultrasound Elasticity Imaging

Classical model-based imaging methods for ultrasound elasticity inverse problem require prior constraints about the underlying elasticity patterns, while finding the appropriate hand-crafted prior for each tissue type is a challenge. In contrast, standard data-driven methods count solely on supervised learning on the training data pairs leading to massive network parameters for unnecessary physical model relearning which might not be consistent with the governing physical models of the imaging system. Fusing the physical forward model and noise statistics with data-adaptive priors leads to a united reconstruction framework that guarantees the learned reconstruction agrees with the physical models while coping with the limited training data. In this paper, we propose a new methodology for estimating the elasticity image by solving a regularized optimization problem which benefits from the physics-based modeling via a data-fidelity term and adversarially learned priors via a regularization term. In this method, the regularizer is trained based on the Wasserstein Generative Adversarial Network (WGAN) objective function which tries to distinguish the distribution of clean and noisy images. Leveraging such an adversarial regularizer for parameterizing the distribution of latent images and using gradient descent (GD) for solving the corresponding regularized optimization task leads to stability and convergence of the reconstruction compared to pixel-wise supervised learning schemes. Our simulation results verify the effectiveness and robustness of the proposed methodology with limited training datasets.
A simple method for sampling random Clifford operators

We describe a simple algorithm for sampling n-qubit Clifford operators uniformly at random. The algorithm outputs the Clifford operators in the form of quantum circuits with at most 5n + 2n2 elementary gates and a maximum depth of ${\mathcal{O}}\left({n\,{\text{log}}\,n}\right)$ on fully connected topologies. The circuit can be output in a streaming fashion as the algorithm proceeds, and different parts of the circuit can be generated in parallel. The algorithm has an ${\mathcal{O}}\left({{n^2}}\right)$ time complexity, which matches the current state of the art. The main advantage of the proposed algorithm, however, lies in its simplicity and elementary derivation.
Why Do Colonial Investments Persist Less in Anglophone than in Francophone Africa

Comparisons of British and French colonialism in Africa have typically focused on institutions (e.g., directness of rule, forced labor, conscription) or the effect of those institutions on post-independence political reforms and development. Instead, this article focuses on colonial public investments in health and education in East and West Africa. I find that such investments are better predictors of today's development in Francophone than in Anglophone Africa. Why? While more political instability (e.g., violent conflict) should decrease persistence, I find---contrary to what might be expected---that Anglophone countries have not been more (or less) unstable since independence, as proxied by their number of coups, government turnovers, and constitutions. Instead, I suggest that the higher economic growth of Anglophone Africa in recent decades (compared to Francophone Africa) is one mechanism that erodes the persistence of colonial investments.
The Interplay of Electrostatics and Chemical Positioning in the Evolution of Antibiotic Resistance in TEM β-Lactamases

The interplay of enzyme active site electrostatics and chemical positioning is important for understanding the origin(s) of enzyme catalysis and the design of novel catalysts. We reconstruct the ev...
Knowing when not to swing: EEG evidence that enhanced perception–action coupling underlies baseball batter expertise

Given a decision that requires less than half a second for evaluating the characteristics of the incoming pitch and generating a motor response, hitting a baseball potentially requires unique perception-action coupling to achieve high performance. We designed a rapid perceptual decision-making experiment modeled as a Go/No-Go task yet tailored to reflect a real scenario confronted by a baseball hitter. For groups of experts (Division I baseball players) and novices (non-players), we recorded electroencephalography (EEG) while they performed the task. We analyzed evoked EEG single-trial variability, contingent negative variation (CNV), and pre-stimulus alpha power with respect to the expert vs. novice groups. We found strong evidence for differences in inhibitory processes between the two groups, specifically differential activity in supplementary motor areas (SMA), indicative of enhanced inhibitory control in the expert (baseball player) group. We also found selective activity in the fusiform gyrus (FG) and orbital gyrus in the expert group, suggesting an enhanced perception-action coupling in baseball players that differentiates them from matched controls. In sum, our results show that EEG correlates of decision formation can be used to identify neural markers of high-performance athletes.
The Lincoln programmable image-processing wafer

The Programmable Image Processor is a laser-restructurable, wafer-scale device fabricated on a 125-mm wafer using an n-well CMOS process with 2.0 micrometer gates. Yield projections indicate that one wafer has enough devices to construct an array of 16 SIMD-programmable processors and 25 shared memories. The memory array can store two images each 128-by-128 pixels. One run of wafers has been fabricated, and these wafers were undergoing testing and restructuring at the time of publication. >
Choice of Law for Multistate Defamation – The State of Affairs as Internet Defamation Beckons

Defamation claims frequently raise potential choice of law issues. This is particularly true when the defamatory statements have been disseminated in several different states. Today the Internet enables anyone with basic computer equipment to become a publisher whose statements can be seen around the world. This phenomenon will undoubtedly add to the number of cases requiring a conflict of laws analysis. This article is primarily a critical survey of the commentary and case law in the United States on choice of law for multistate defamation. Part I focuses on commentary pre-dating the Internet, reflecting numerous suggestions on this issue. It also includes discussion of provisions of the first and second Restatement of Conflict of Laws. Part II surveys and critiques recent cases resolving choice of laws issues under the various approaches now taken by American courts. Part III makes some broader observations on the state of current case law. Finally, Part IV discusses whether choice of law analyses will need to be modified in the context of defamation on the Internet.
Identification of RNA Binding Proteins by UV Cross‐Linking

One of the major focuses of modern biology is to understand the dynamics of RNA-protein interactions, including factors that interact with mRNA, rRNA, tRNA, snRNA, hnRNA, siRNA, and viral RNA. Identification the direct interactions between proteins and RNA has greatly advanced our knowledge about the function of RNA-protein machines. UV crosslinking is a straightforward and powerful tool in this endeavor.
Delay of Gratification and Delay Discounting: A Unifying Feedback Model of Delay-Related Impulsive Behavior

Delay of Gratification (DG) and Delay Discounting (DD) represent two indices of impulsive behavior often treated as though they represent equivalent or the same underlying processes. However, there are key differences between DG and DD procedures, and between certain research findings with each procedure, that suggest they are not equivalent. In the current article, evidence is presented to support the argument that DG and DD measure discrete, yet related, processes involved in delay-related impulsive behavior. Also presented is a theoretical “feedback model” for the relation between DG and DD. In the model, it is proposed that the processes measured by DG are less cognitive and less learning-mediated than those measured by DD. However, as proposed, ability to sustain choices for delayed rewards (DG) is still represented in the choice processes measured by DD through an individual’s learning history with DG types of situations; that is, the less a person is able to sustain choices for delayed rewards the more likely he or she will be to choose immediate rewards when given choices between larger delayed and smaller but more immediate options. The proposed model is consonant with observed consistencies and differences between DG and DD measures. From the proposed model, new research questions arise that would be lost in a continued conceptualization of DG and DD as equivalent measures.
Lipids link ion channels and cancer

How does membrane voltage control cellular proliferation? This is a key but poorly understood step in understanding how dysregulation of the electrical balance in a cell can lead to uncontrolled proliferation and, eventually, to tumor development. Although the phenomenon is well established ( 1 – 3 ), the underlying mechanisms have been unclear. On page 873 of this issue, Zhou et al. ( 4 ) show that persistent changes in the resting membrane potential (the voltage across the membrane of a cell), caused by the uncontrolled expression of ion channels, can cause negatively charged lipid in the inner membrane leaflet to cluster and attract the signaling protein K-Ras, enhancing its ability to promote cell proliferation.
Developmental Biology of Fern Gametophytes: Developmental physiology of gametophytes – the germ filament

In all homosporous ferns, except those included in the Marattiaceae and Osmundaceae, the protonema initial generates a uniseriate, elongate filament composed of a varying number of chlorophyllous cells. All division planes leading up to the formation of the filament are oriented perpendicular to the long axis of the cell and thus lie parallel to one another. At each division, new walls are laid down between cells to hold them together as an elongating filament. When the filament has produced a certain number of cells, as determined by the conditions of growth and other factors, its terminal or subterminal cell divides by a wall oblique to the long axis to give rise to a bidirectionally dividing plate of cells. This marks the beginning of planar or prothallial growth. By the continued meristematic activity of this cell, aided later by the establishment of a pluricellular meristem, a flat, often cordate or exceptionally, ribbon-shaped, structure is formed. Rarely, as in Schizaea and Trichomanes (Hymenophyllaceae), no change ever occurs in the plane of cell divisions of the germ filament, which thus retains the basic filamentous morphology throughout the gametophytic phase. In this book, the terms ‘germ filament’, ‘protonemal filament’ or ‘protonema’ (plural, protonemata) are applied to the filamentous gametophyte and the term ‘prothallus’ is used to denote the structure with the planar morphology.
Late-onset temporal lobe epilepsy in a patient with juvenile myoclonic epilepsy

We report a patient with longstanding, severe juvenile myoclonic epilepsy who subsequently developed features of temporal lobe epilepsy, which gradually became clinically dominant. Over the years, there was an electrographic evolution from the typical generalised epileptiform patterns, characteristic of juvenile myoclonic epilepsy, to the novel appearance of interictal temporal spikes immediately preceding bisynchronous discharges, and subsequently to temporal intermittent rhythmic delta activity and temporal lobe-onset seizures. In this rare case of coexistent primary generalised epilepsy and focal epilepsy, the epileptic networks of the two forms of epilepsy appear to overlap.
Do People Like Nudges

In recent years, there has been a great deal of debate about the ethical questions associated with “nudges,” understood as approaches that steer people in certain directions while maintaining their freedom of choice. Evidence about people’s views cannot resolve the ethical questions, but in democratic societies (and nondemocratic ones as well), those views will inevitably affect what public officials are willing to do. Existing evidence, including a nationally representative survey, supports six general conclusions. First, there is widespread support for nudges of the kind that democratic societies have adopted or seriously considered in the recent past; surprisingly, that support can be found across partisan lines. While people tend to have serious objections to mandates as such, they do not have similar objections to nudges. Second, the support evaporates when people suspect the motivations of those who are engaged in nudging, and when they fear that because of inertia and inattention, citizens might end up with outcomes that are inconsistent with their interests or their values. Third, there appears to be somewhat greater support for nudges that appeal to conscious, deliberative thinking than for nudges that affect subconscious or unconscious processing, though this conclusion is highly qualified, and there can be widespread approval of the latter as well (especially if they are meant to combat self-control problems). Fourth, people’s assessment of nudges in general will be greatly affected by the political valence of the particular nudges that they have in mind (or that are brought to their minds). Fifth, transparency about nudging will not, in general, reduce the effectiveness of nudges, because most nudges are already transparent, and because people will not, in general, rebel against nudges. Sixth, there is preliminary but suggestive evidence of potential “reactance” against certain nudges.
Quantitative analysis of Munsell color data from archeological ceramics

Abstract The Munsell color system is almost universally used for measuring colors of archeological artifacts. In addition to recording soil colors, many use the Munsell system to record the colors of ceramic attributes, such as pastes, slips, glazes, and paints. These data are often used in both modal and typological analyses of ceramic style. Most often, however, Munsell color data are used in a purely descriptive fashion, and the quantitative potential of the information is not fully exploited. We propose here a new protocol for manipulating, analyzing, and interpreting Munsell color data that permits the statistical investigation of sets of Munsell observations as well as hypothesis testing. A Munsell color reading is composed of three continuous interval scale variables, and these data can be transformed into x , y , z coordinates that define a location in color space (D'Andrade and Romney, 2003). Once transformed into spatial coordinates, Munsell data can be analyzed using spatial analysis techniques, such as k -means cluster analysis, as well as non-spatial statistical methods. In our case, we chose to use logistic regression to study the degree to which color could differentiate ceramic types and varieties. We argue that logistic regression is an appropriate approach for testing common types of hypotheses that archeologists may pose with ceramic color data. We illustrate the approach on sets of Munsell data representing ceramic slip colors from Mayapan, Yucatan, Mexico. We were able to show a clear separation between the Mama Red and Polbox Buff types. Using the techniques we suggest here, archeologists can significantly expand their modal analyses of ceramics because color is a common and easily measured attribute in most ceramic assemblages. The same techniques can easily be extended to other kinds of artifacts, including lithics and textiles, as well as soils, sediments, plants, and other objects studied by field scientists.