cssi/SciSciGPT-SciSciNet huggingface dataset:
The relationship of teacher ratings of executive functions to emergent literacy in Head Start

The purpose of this study was to investigate the concurrent relationships between selected teacher-rated executive function (EF) and a comprehensive array of emergent literacy skills in preschool children after adjusting for targeted covariates including at-risk status. The sample comprised 114 three-year-olds who were attending Head Start preschool. The teacher-completed Behavior Rating Inventory of Executive Function-Preschool was used to generate three predictor variables: Inhibitory Self-Control, Flexibility, and Emergent Metacognition. The emergent literacy outcomes included the Teacher Ratings of Oral Language and Literacy Reading, Writing, and Language scales, Stanford Binet Intelligence Scale 5 Vocabulary Subtest, Test of Early Reading-3 Convention Subtest, and Print Knowledge and Phonological Awareness subtests from the Test of Preschool Early Literacy. Findings revealed that after controlling for selected covariates (age, gender, classroom) and at-risk status, when the three EF indices were included in the model the amount of variance significantly increased for all of the emergent literacy outcomes, with the total variance accounted for ranging from 32% (Phonological Awareness) to 59% (pre-Writing). Emergent Metacognition was a significant predictor for nearly every outcome except Phonological Awareness and Reading Conventions; Flexibility and Inhibitory Self-Control were significant predictors of Language. Significant interactions were present for at-risk status and teacher ratings of both Flexibility and Inhibitory on TERA-3 Conventions, but these findings appeared to be affected more by the not at-risk group than the at-risk group. Results reinforce the potential added importance of teacher ratings of EF, particularly with respect to the integrity of early developing EF, in the prediction of emergent literacy skills in young preschool children.
Genetic and physiological mechanisms of freezing tolerance in locally adapted populations of a winter annual.

Premise Despite myriad examples of local adaptation, the phenotypes and genetic variants underlying such adaptive differentiation are seldom known. Recent work on freezing tolerance and local adaptation in ecotypes of Arabidopsis thaliana from Italy and Sweden provides an essential foundation for uncovering the genotype-phenotype-fitness map for an adaptive response to a key environmental stress. Methods We examined the consequences of a naturally occurring loss-of-function (LOF) mutation in an Italian allele of the gene that encodes the transcription factor CBF2, which underlies a major freezing-tolerance locus. We used four lines with a Swedish genetic background, each containing a LOF CBF2 allele. Two lines had introgression segments containing the Italian CBF2 allele, and two contained deletions created using CRISPR-Cas9. We used a growth chamber experiment to quantify freezing tolerance and gene expression before and after cold acclimation. Results Freezing tolerance was lower in the Italian (11%) compared to the Swedish (72%) ecotype, and all four experimental CBF2 LOF lines had reduced freezing tolerance compared to the Swedish ecotype. Differential expression analyses identified 10 genes for which all CBF2 LOF lines, and the IT ecotype had similar patterns of reduced cold responsive expression compared to the SW ecotype. Conclusions We identified 10 genes that are at least partially regulated by CBF2 that may contribute to the differences in cold-acclimated freezing tolerance between the Italian and Swedish ecotypes. These results provide novel insight into the molecular and physiological mechanisms connecting a naturally occurring sequence polymorphism to an adaptive response to freezing conditions.
Tensor algebras of product systems and their C⁎-envelopes

Abstract Let ( G , P ) be an abelian, lattice ordered group and let X be a compactly aligned product system over P with coefficients in A . We show that the C*-envelope of the Nica tensor algebra N T X + coincides with both Sehnem's covariance algebra A × X P and the co-universal C ⁎ -algebra N O X r for injective, gauge-compatible, Nica-covariant representations of Carlsen, Larsen, Sims and Vittadello. We give several applications of this result on both the selfadjoint and non-selfadjoint operator algebra theory. First we guarantee the existence of N O X r , thus settling a problem of Carlsen, Larsen, Sims and Vittadello which was open even for abelian, lattice ordered groups. As a second application, we resolve a problem posed by Skalski and Zacharias on dilating isometric representations of product systems to unitary representations. As a third application we characterize the C ⁎ -envelope of the tensor algebra of a finitely aligned higher-rank graph which also holds for topological higher-rank graphs. As a final application we prove reduced Hao-Ng isomorphisms for generalized gauge actions of discrete groups on C ⁎ -algebras of product systems. This generalizes recent results that were obtained by various authors in the case where ( G , P ) = ( Z , N ) .
Bound-State Diffusion due to Binding to Flexible Polymers in a Selective Biofilter

Abstract Selective biofilters are used by cells to control the transport of proteins, nucleic acids, and other macromolecules. Biological filters demonstrate both high specificity and rapid motion or high flux of proteins. In contrast, high flux comes at the expense of selectivity in many synthetic filters. Binding can lead to selective transport in systems in which the bound particle can diffuse, but the mechanisms that lead to bound diffusion remain unclear. Previous theory has proposed a molecular mechanism of bound-state mobility based only on transient binding to flexible polymers. However, this mechanism has not been directly tested in experiments. We demonstrate that bound mobility via tethered diffusion can be engineered into a synthetic gel using protein fragments derived from the nuclear pore complex. The resulting bound-state diffusion is quantitatively consistent with theory. Our results suggest that synthetic biological filters can be designed to take advantage of tethered diffusion to give rapid, selective transport.
Multi-cell Monte Carlo method for phase prediction

We propose a Multi-Cell Monte Carlo algorithm, or (MC)$${}^{2}$$, for predicting stable phases in chemically complex crystalline systems. This algorithm takes advantage of multiple cells to represent possible phases, while eliminating the size and concentration restrictions in the previous counterparts. Free atomic transfer among cells is achieved via the application of the lever rule, where an assigned molar ratio virtually controls the percentage of each cell in the overall simulation, making (MC)$${}^{2}$$ the first successful algorithm for simulating phase coexistence in crystalline solids. During the application of this method, all energies are directly computed via density functional theory calculations. We test the method by successful prediction of the stable phases of known binary systems. We then apply the method to a quaternary high-entropy alloy. The method is particularly powerful in predicting stable phases of multicomponent systems, for which phase diagrams do not exist.
New classes of set-sequential trees

Abstract A graph is called set-sequential if its vertices can be labeled with distinct nonzero vectors in F 2 n such that when each edge is labeled with the sum ( mod 2 ) of its vertices, every nonzero vector in F 2 n is the label for either a single vertex or a single edge. We resolve certain cases of a conjecture of Balister, Győri, and Schelp in order to show many new classes of trees to be set-sequential. We show that all caterpillars T of diameter k such that k ≤ 18 or | V ( T ) | ≥ 2 k − 1 are set-sequential, where T has only odd-degree vertices and | V ( T ) | = 2 n − 1 for some positive integer n . We also present a new method of recursively constructing set-sequential trees.
Algebraic Integrability of Macdonald Operators and Representations of Quantum Groups

In this paper we construct examples of commutative rings of difference operators with matrix coefficients from representation theory of quantum groups, generalizing the results of our previous paper [ES] to the q-deformed case. A generalized Baker–Akhiezer function Ψ is realized as a matrix character of a Verma module and is a common eigenfunction for a commutative ring of difference operators. In particular, we obtain the following result in Macdonald theory: at integer values of the Macdonald parameter k, there exist difference operators commuting with Macdonald operators which are not polynomials of Macdonald operators. This result generalizes an analogous result of Chalyh and Veselov for the case q=1, to arbitrary q. As a by-product, we prove a generalized Weyl character formula for Macdonald polynomials (= Conjecture 8.2 from [FV]), the duality for the Ψ-function, and the existence of shift operators.
Estimation of Absolute Scale in Monocular SLAM Using Synthetic Data

This paper addresses the problem of scale estimation in monocular SLAM by estimating absolute distances between camera centers of consecutive image frames. These estimates would improve the overall performance of classical (not deep) SLAM systems and allow metric feature locations to be recovered from a single monocular camera. We propose several network architectures that lead to an improvement of scale estimation accuracy over the state of the art. In addition, we exploit a possibility to train the neural network only with synthetic data derived from a computer graphics simulator. Our key insight is that, using only synthetic training inputs, we can achieve similar scale estimation accuracy as that obtained from real data. This fact indicates that fully annotated simulated data is a viable alternative to existing deep-learning-based SLAM systems trained on real (unlabeled) data. Our experiments with unsupervised domain adaptation also show that the difference in visual appearance between simulated and real data does not affect scale estimation results. Our method operates with low-resolution images (0.03 MP), which makes it practical for real-time SLAM applications with a monocular camera.
Efficient Integration of Compiler-Directed Cache Coherence and Data Prefetching

Cache coherence enforcement and memory latency reduction and hiding are very important and challenging problems in the design of large-scale distributed shared-memory (DSM) multiprocessors. We propose an integrated approach to solve these problems through a compiler-directed cache coherence scheme called the Cache Coherence with Data Prefetching (CCDP) scheme. The CCDP scheme enforces cache coherence by prefetching the potentially stale references in a parallel program. It also prefetches the non-stale references to hide their memory latencies. To optimize the performance of the CCDP scheme, some prefetch hardware support is provided to efficiently handle these two forms of data prefetching operations. We also developed the compiler techniques utilized by the CCDP scheme for stale reference detection, prefetch target analysis, and prefetch scheduling. We evaluated the performance of the CCDP scheme via execution-driven simulations of several numerical applications from the SPEC CFP95 and the Perfect benchmark suites. The simulation results show that the CCDP scheme provides significant performance improvements for the applications studied, comparable to that obtained with a full-map hardware cache coherence scheme.
Scalable Parallel Matrix Multiplication on Distributed Memory Parallel Computers

Consider any known sequential algorithm for matrix multiplication over an arbitrary ring with time complexity O(N?), where 2<??3. We show that such an algorithm can be parallelized on a distributed memory parallel computer (DMPC) in O(logN) time by using N?/logN processors. Such a parallel computation is cost optimal and matches the performance of PRAM. Furthermore, our parallelization on a DMPC can be made fully scalable, that is, for all 1?p?N?/logN, multiplying two N×N matrices can be performed by a DMPC with p processors in O(N?/p) time, i.e., linear speedup and cost optimality can be achieved in the range 1..N?/logN]. This unifies all known algorithms for matrix multiplication on DMPC, standard or non- standard, sequential or parallel. Extensions of our methods and results to other parallel systems are also presented. For instance, for all 1?p? N? /logN, multiplying two N×N matrices can be performed by p processors connected by a hypercubic network in O(N?/p+(N2/p2/?)(logp)2(??1)/?) time, which implies that if p=O(N?/(logN)2(??1)/(??2)), linear speedup can be achieved. Such a parallelization is highly scalable. The above claims result in significant progress in scalable parallel matrix multiplication (as well as solving many other important problems) on distributed memory systems, both theoretically and practically.
SqueezeFit: Label-Aware Dimensionality Reduction by Semidefinite Programming

Given labeled points in a high-dimensional vector space, we seek a low-dimensional subspace such that projecting onto this subspace maintains some prescribed distance between points of differing labels. Intended applications include compressive classification. Taking inspiration from large margin nearest neighbor classification, this paper introduces a semidefinite relaxation of this problem. Unlike its predecessors, this relaxation is amenable to theoretical analysis, allowing us to provably recover a planted projection operator from the data.
Profiling and structural analysis of cardenolides in two species of Digitalis using liquid chromatography coupled with high-resolution mass spectrometry

Abstract Plants of the Digitalis genus contain a cocktail of cardenolides commonly prescribed to treat heart failure. Cardenolides in Digitalis extracts have been conventionally quantified by high-performance liquid chromatography yet the lack of structural information compounded with possible co-eluents renders this method insufficient for analyzing cardenolides in plants. The goal of this work is to structurally characterize cardiac glycosides in fresh-leaf extracts using liquid chromatography coupled with tandem mass spectrometry (LC/MS/MS) that provides measured accurate mass. Fragmentation of cardenolides is featured by sequential loss of sugar units while the steroid aglycone moieties undergo stepwise elimination of hydroxyl groups, which distinguishes different aglycones. Using a reverse-phase LC column, the sequence of elution follows: diginatigenin→digoxigenin→gitoxigenin→gitaloxigenin→digitoxigenin for cardenolides with the same sugar units but different aglycones. A linear range of 0.8-500 ng ml−1 has been achieved for digoxigenin, β-acetyldigoxin, and digitoxigenin with limits of detection ranging from 0.09 to 0.45 ngml−1. A total of seventeen cardenolides have been detected with lanatoside A, C, and E as major cardenolides in Digitalis lanata while seven have been found in Digitalis purpurea including purpurea glycoside A, B, and E. Surprisingly, glucodigifucoside in D. lanata and verodoxin and digitoxigenin fucoside in D. purpurea have also been found as major cardenolides. As the first MS/MS-based method developed for analyzing cardenolides in plant extracts, this method serves as a foundation for complete identification and accurate quantification of cardiac glycosides, a necessary step towards understanding the biosynthesis of cardenolide in plants.
Quantitative analysis of 1300-nm three-photon calcium imaging in the mouse brain

1300 nm three-photon calcium imaging has emerged as a useful technique to allow calcium imaging in deep brain regions. Application to large-scale neural activity imaging entails a careful balance between recording fidelity and perturbation to the sample. We calculated and experimentally verified the excitation pulse energy to achieve the minimum photon count required for the detection of calcium transients in GCaMP6s-expressing neurons for 920 nm two-photon and 1320 nm three-photon excitation. By considering the combined effects of in-focus signal attenuation and out-of-focus background generation, we quantified the cross-over depth beyond which three-photon microscopy outpeforms two-photon microscopy in recording fidelity. Brain tissue heating by continuous three-photon imaging was simulated with Monte Carlo method and experimentally validated with immunohistochemistry. Increased immunoreactivity was observed with 150 mW excitation power at 1 and 1.2 mm imaging depths. Our analysis presents a translatable model for the optimization of three-photon calcium imaging based on experimentally tractable parameters.
Fast and Robust 3-D Sound Source Localization with DSVD-PHAT

This paper introduces a variant of the Singular Value Decomposition with Phase Transform (SVD-PHAT), named Difference SVD-PHAT (DSVD-PHAT), to achieve robust Sound Source Localization (SSL) in noisy conditions. Experiments are performed on a Baxter robot with a four-microphone planar array mounted on its head. Results show that this method offers similar robustness to noise as the state-of-the-art Multiple Signal Classification based on Generalized Singular Value Decomposition (GSVD-MUSIC) method, and considerably reduces the computational load by a factor of 250. This performance gain thus makes DSVD-PHAT appealing for real-time application on robots with limited on-board computing power.
A note on Alberti’s Luzin-type theorem for gradients

We give a “soft” proof of Alberti’s Luzin-type theorem in Alberti (J Funct Anal 100:110–118, 1991), using elementary geometric measure theory and topology. Applications to the $$C^2$$-rectifiability problem are also discussed.
Bacterial contribution to genesis of the novel germ line determinant oskar

New cellular functions and developmental processes can evolve by modifying existing genes or creating novel genes. Novel genes can arise not only via duplication or mutation but also by acquiring foreign DNA, also called horizontal gene transfer (HGT). Here we show that HGT likely contributed to the creation of a novel gene indispensable for reproduction in some insects. Long considered a novel gene with unknown origin, oskar has evolved to fulfil a crucial role in insect germ cell formation. Our analysis of over 100 insect Oskar sequences suggests that oskar arose de novo via fusion of eukaryotic and prokaryotic sequences. This work shows that highly unusual gene origin processes can give rise to novel genes that may facilitate evolution of novel developmental mechanisms.
Critical thresholds in one-dimensional damped Euler–Poisson systems

This paper is concerned with the critical threshold phenomenon for one-dimensional damped, pressureless Euler–Poisson equations with electric force induced by a constant background, originally stud...
Real-Time Soft Body 3D Proprioception via Deep Vision-Based Sensing

Soft bodies made from flexible and deformable materials are popular in many robotics applications, but their proprioceptive sensing has been a long-standing challenge. In other words, there has hardly been a method to measure and model the high-dimensional 3D shapes of soft bodies with internal sensors. We propose a framework to measure the high-resolution 3D shapes of soft bodies in real-time with embedded cameras. The cameras capture visual patterns inside a soft body, and a convolutional neural network (CNN) produces a latent code representing the deformation state, which can then be used to reconstruct the body's 3D shape using another neural network. We test the framework on various soft bodies, such as a Baymax-shaped toy, a latex balloon, and some soft robot fingers, and achieve real-time computation ( $\leq$ 2.5 ms/frame) for robust shape estimation with high precision ( $\leq$ 1% relative error) and high resolution. We believe the method could be applied to soft robotics and human-robot interaction for proprioceptive shape sensing. Our code is available at: https://ai4ce.github.io/DeepSoRo/ .
Neural mechanisms of economic choices in mice

Economic choices entail computing and comparing subjective values. Evidence from primates indicates that this behavior relies on the orbitofrontal cortex. Conversely, previous work in rodents provided conflicting results. Here we present a mouse model of economic choice behavior, and we show that the lateral orbital (LO) area is intimately related to the decision process. In the experiments, mice chose between different juices offered in variable amounts. Choice patterns closely resembled those measured in primates. Optogenetic inactivation of LO dramatically disrupted choices by inducing erratic changes of relative value and by increasing choice variability. Neuronal recordings revealed that different groups of cells encoded the values of individual options, the binary choice outcome and the chosen value. These groups match those previously identified in primates, except that the neuronal representation in mice is spatial (in monkeys it is good-based). Our results lay the foundations for a circuit-level analysis of economic decisions.
Small-molecule covalent bond formation at tyrosine creates a binding site and inhibits activation of Ral GTPases.

Ral (Ras-like) GTPases are directly activated by oncogenic Ras GTPases. Mutant K-Ras (G12C) has enabled the development of covalent K-Ras inhibitors currently in clinical trials. However, Ral, and the overwhelming majority of mutant oncogenic K-Ras, are devoid of a druggable pocket and lack an accessible cysteine for the development of a covalent inhibitor. Here, we report that covalent bond formation by an aryl sulfonyl fluoride electrophile at a tyrosine residue (Tyr-82) inhibits guanine exchange factor Rgl2-mediated nucleotide exchange of Ral GTPase. A high-resolution 1.18-A X-ray cocrystal structure shows that the compound binds to a well-defined binding site in RalA as a result of a switch II loop conformational change. The structure, along with additional high-resolution crystal structures of several analogs in complex with RalA, confirm the importance of key hydrogen bond anchors between compound sulfone oxygen atoms and Ral backbone nitrogen atoms. Our discovery of a pocket with features found on known druggable sites and covalent modification of a bystander tyrosine residue present in Ral and Ras GTPases provide a strategy that could lead to therapeutic agent targeting oncogenic Ras mutants that are devoid of a cysteine nucleophile.
Mitigation of Civilian-to-Military Interference in DSRC for Urban Operations

Dedicated short-range communications (DSRC) attracts popularity in the military applications thanks to its easiness in establishment, no need for paid subscription, and wide compatibility with any other IEEE 802.11 standards. The main challenge in DSRC is congestion due to existence of only 7 channels, which may not be enough to accommodate the increased number of transmitters expected to be deployed in the near future. Recently, there are a myriad of urban operation scenarios for the military including urban warfare and humanitarian assistance/disaster relief (HA/DR). The key challenge is that the communications among the military vehicles can be interfered by civilian users. It is the desire that the messages transmitted by the military vehicles hold a higher significance so that they can avoid the interference coming from the civilian users, which is not supported in the current version of DSRC. As a remedy, this paper proposes a protocol that prioritizes the military DSRC users while muffling the civilian DSRC users. Our results show that this prioritization method achieves higher communications performances for the military DSRC users.
Stably Noetherian Algebras of Polynomial Growth

Let A be a right noetherian algebra over a field k. If the base field extension A ⊗kK remains right noetherian for all extension fields K of k, then A is called stably right noetherian over k. We develop an inductive method to show that certain algebras of finite Gelfand-Kirillov dimension are stably noetherian, using critical composition series. We use this to characterize which algebras satisfying a polynomial identity are stably noetherian. The method also applies to many $\mathbb {N}$ -graded rings of finite global dimension; in particular, we see that a noetherian Artin-Schelter regular algebra must be stably noetherian. In addition, we study more general variations of the stably noetherian property where the field extensions are restricted to those of a certain type, for instance purely transcendental extensions.
Telomere length heterogeneity in ALT cells is maintained by PML-dependent localization of the BTR complex to telomeres

Telomeres consist of TTAGGG repeats bound by protein complexes that serve to protect the natural end of linear chromosomes. Most cells maintain telomere repeat lengths by using the enzyme telomerase, although there are some cancer cells that use a telomerase-independent mechanism of telomere extension, termed alternative lengthening of telomeres (ALT). Cells that use ALT are characterized, in part, by the presence of specialized PML nuclear bodies called ALT-associated PML bodies (APBs). APBs localize to and cluster telomeric ends together with telomeric and DNA damage factors, which led to the proposal that these bodies act as a platform on which ALT can occur. However, the necessity of APBs and their function in the ALT pathway has remained unclear. Here, we used CRISPR/Cas9 to delete PML and APB components from ALT-positive cells to cleanly define the function of APBs in ALT. We found that PML is required for the ALT mechanism, and that this necessity stems from APBs' role in localizing the BLM-TOP3A-RMI (BTR) complex to ALT telomere ends. Strikingly, recruitment of the BTR complex to telomeres in a PML-independent manner bypasses the need for PML in the ALT pathway, suggesting that BTR localization to telomeres is sufficient to sustain ALT activity.
An N-Path Filter with Multiphase PWM Clocks for Harmonic Response Suppression

A switched-capacitor N-path circuit can be employed for filtering an RF signal, as well as a passive downcon-verter. A known limitation of a switch-based N-path filter is that in addition to downconverting signals around the desired center frequency, the circuit also downconverts signals located around harmonics of the LO frequency. An N-path filter that uses a PWM representation of a sinusoidal LO to mitigate harmonic downconversion is proposed in this work. Single-edge, natural-sampling pulse-width modulated (PWM) clocks are used to drive the switches in the N-path filter. The potential for employing PWM for providing gain control is also described.
The Zero Set of a Real Analytic Function

A brief proof of the statement that the zero-set of a nontrivial real-analytic function in $d$-dimensional space has zero measure is provided.
Enabling continuous operations for UAVs with an autonomous service network infrastructure

One of the major restrictions on the practical applications of unmanned aerial vehicles (UAV) is their incomplete self-sufficiency, which makes continuous operations infeasible without human oversights. The more oversight UAVs require, the less likely they are going to be commercially advantageous when compared to their alternatives. As an autonomous system, how much human interaction is needed to function is one of the best indicators evaluating the limitations and inefficiencies of the UAVs. Popular UAV related research areas, such as path planning and computer vision, have enabled substantial advances in the ability of drones to act on their own. This research is dedicated to in-flight operations, in which there is not much reported effort to tackle the problem from the aspect of the supportive infrastructure. In this paper, an Autonomous Service network infrastructure (AutoServe) is proposed. Aiming at increasing the future autonomy of UAVs, the AutoServe system includes a service-oriented landing platform and a customized communication protocol. This supportive AutoServe infrastructure will autonomize many tasks currently done manually by human operators, such as battery replacement. A proof-of-concept prototype has been built and the simulation experimental study validated the design.
Measuring Founding Strategy

We propose an approach to measure strategy using text-based machine learning. The key insight is that distance in the statements made by companies can be partially indicative of their strategic positioning with respect to each other. We formalize this insight by proposing a new measure of strategic positioning — the strategy score — and defining the assumptions and conditions under which we can estimate it empirically. We then implement this approach to score the strategic positioning of a large sample of startups in Crunchbase in relation to contemporaneous public companies. Startups with a higher founding strategy score have higher equity outcomes, reside in locations with more venture capital, and receive a higher amount of financing in seed financing events. One implication of this result is that founding strategic positioning is important for startup performance.
Perchlorate and volatiles of the brine of Lake Vida (Antarctica): Implication for the in situ analysis of Mars sediments

The cold (−13.4°C), cryoencapsulated, anoxic, interstitial brine of the >27 m thick ice of Lake Vida (Victoria Valley, Antarctica) contains 49 µg · L−1 of perchlorate and 11 µg · L−1 of chlorate. Lake Vida brine (LVBr) may provide an analog for potential oxychlorine-rich subsurface brine on Mars. LVBr volatiles were analyzed by solid-phase microextraction (SPME) gas chromatography–mass spectrometry (GC-MS) with two different SPME fibers. With the exception of volatile organic sulfur compounds, most other volatiles observed were artifacts produced in the GC injector when the thermal decomposition products of oxychlorines reacted with reduced carbon derived from LVBr and the SPME fiber phases. Analysis of MilliQ water with perchlorate (40 µg · L−1) showed low level of organic artifacts, reflecting carbon limitation. In order to observe sample-derived organic compounds, both in analog samples and on Mars, the molar abundance of reduced carbon in a sample must exceed those of O2 and Cl2 produced during decomposition of oxychlorines. This suggests that the abundance of compounds observed by the Sample Analysis at Mars (SAM) instruments in Sheepbed samples (CB-3, CB5, and CB6) may be controlled by an increase in the reduced-carbon/oxychlorine ratio of these samples. To increase chances of in situ detection of Martian organics during pyrolysis-GC-MS, we propose that the derivatization agents stored on SAM may be used as an external source of reduced carbon, increasing artificially the reduced-carbon to perchlorate ratio during pyrolysis, allowing the expression of more abundant and perhaps more diverse Martian organic matter.
Chapter 3 – Introduction to MATLAB Programming

The chapter, “Introduction to MATLAB Programming” introduces the idea of algorithms and scripts. Programming style, such as the top-down design approach, and use of commenting for documentation, are explained. Input and output statements are introduced, including the formatting of output. Simple plot functions and functions that annotate plots are introduced in this chapter as it is easier to produce plots using scripts. Reading from a file using load and writing to a file using save is demonstrated. Finally, the concept of a user-defined function is introduced with the type of function that calculates and returns a single value.
The Economics of Networks

I analyze the salient features of networks and point out the similarities between the economic structure of networks and the structure of vertically related industries. The analysis focuses on positive consumption and production externalities, commonly called network externalities. I discuss their sources and their effects on pricing and market structure. I distinguish between results that do not depend on the underlying industry microstructure (the 'macro' approach) and those that do (the 'micro' approach). I analyze the issues of compatibility, coordination to technical standards, interconnection and interoperability, and their effects on pricing and quality of services and on the value of network links in various ownership structures. I also briefly discuss the issue of interconnection fees for bottleneck facilities.
Case–Control Study, Hospital-Based †

A hospital-based case–control study is a case–control study in which cases with a given disease are selected from persons with that disease in a given hospital or group of hospitals, and controls are patients with other diseases from these hospitals, the strengths and weaknesses of hospital-based control studies are discussed in comparison to population-based case–control studies. Keywords: case–control study; hospital-based case–control study; population-based; bias in case–control studies
Device-Circuit-Architecture Co-Exploration for Computing-in-Memory Neural Accelerators

Co-exploration of neural architectures and hardware design is promising due to its capability to simultaneously optimize network accuracy and hardware efficiency. However, state-of-the-art neural architecture search algorithms for the co-exploration are dedicated for the conventional von-Neumann computing architecture, whose performance is heavily limited by the well-known memory wall. In this article, we are the first to bring the computing-in-memory architecture, which can easily transcend the memory wall, to interplay with the neural architecture search, aiming to find the most efficient neural architectures with high network accuracy and maximized hardware efficiency. Such a novel combination makes opportunities to boost performance, but also brings a bunch of challenges: The optimization space spans across multiple design layers from device type and circuit topology to neural architecture; and the presence of device variation may drastically degrade the neural network performance. To address these challenges, we propose a cross-layer exploration framework, namely NACIM, which jointly explores device, circuit and architecture design space and takes device variation into consideration to find the most robust neural architectures, coupled with the most efficient hardware design. Experimental results demonstrate that NACIM can find the robust neural network with 0.45 percent accuracy loss in the presence of device variation, compared with a 76.44 percent loss from the state-of-the-art NAS without consideration of variation; in addition, NACIM achieves an energy efficiency up to 16.3 TOPs/W, 3.17× higher than the state-of-the-art NAS.
Inefficiently Low Screening with Walrasian Markets

Abstract Financial intermediaries devote resources to finding and screening borrowers before lending capital. By retaining only sufficiently good matches, informed lenders exacerbate adverse selection problems for others lending in the same market. Failure to internalize this implies that informed lenders are too selective in the matches they retain. The resulting under-use of capital pushes the cost of capital down, decreasing the benefit of being informed rather than uninformed and prompting a reallocation of resources from screening to matching. Compared to the constrained efficient allocation, the decentralized equilibrium has too little screening, too little informed credit, and too much uninformed credit.
Chapter 30 – Employment Law

A community pharmacist begins professional life as an employee and the relationship between employer and employee plays a critical role in the life of a pharmacy. Pharmacists who are employers or employees need to have a good understanding of the law in this area, and this chapter analyses the central issues which arise. The rules are complex, and the chapter includes sections on discrimination.
Glomerulopathy Associated With Cytomegalovirus Viremia in Renal Allografts

Abstract We investigated the relation between cytomegalovirus (CMV) infection and renal-allograft dysfunction in 14 patients. In seven instances (including two successive transplants in one patient), allograft dysfunction occurred during clinically manifest, viremic CMV infection. In five of these, biopsies revealed little or no tubulointerstitial change but a distinctive, diffuse glomerulopathy characterized by enlargement or necrosis of endothelial cells and accumulation of mononuclear cells and fibrillar material in glomerular capillaries. Two of these allografts recovered their function, both with cessation of high-dose immunosuppression. Biopsies in the other 10 patients revealed predominantly tubulointerstitial changes typical of cellular rejection, and most of these patients did not have viremia. One additional patient, studied prospectively, manifested both forms of allograft injury: tubulointerstitial changes occurring two weeks after transplantation and responding to increased immunosuppression,...
Learning under Diverse World Views: Model-Based Inference

People reason about uncertainty with deliberately incomplete models. How do people hampered by different, incomplete views of the world learn from each other? We introduce a model of "model-based inference." Model-based reasoners partition an otherwise hopelessly complex state space into a manageable model. Unless the differences in agents' models are trivial, interactions will often not lead agents to have common beliefs or beliefs near the correct-model belief. If the agents' models have enough in common, then interacting will lead agents to similar beliefs, even if their models also exhibit some bizarre idiosyncrasies and their information is widely dispersed.
An accelerometer-only algorithm for determining the acceleration field of a rigid body, with application in studying the mechanics of mild traumatic brain injury

Abstract We present an algorithm for determining the acceleration field of a rigid body using measurements from four tri-axial accelerometers. The acceleration field is an important quantity in bio-mechanics problems, especially in the study of mild traumatic brain injury (mTBI). The in vivo strains in the brain, which are hypothesized to closely correlate with brain injury, are generally not directly accessible outside of a laboratory setting. However, they can be estimated on knowing the head’s acceleration field. In contrast to other techniques, the proposed algorithm uses data exclusively from accelerometers, rather than from a combination of accelerometers and gyroscopes. For that reason, the proposed accelerometer only (AO) algorithm does not involve any numerical differentiation of data, which is known to greatly amplify measurement noise. For applications where only the magnitude of the acceleration vector is of interest, the algorithm is straightforward, computationally efficient and does not require computation of angular velocity or orientation. When both the magnitude and direction of acceleration are of interest, the proposed algorithm involves the calculation of the angular velocity and orientation as intermediate steps. In addition to helping understand the mechanics of mTBI, the AO-algorithm may find widespread use in several bio-mechanical applications, gyroscope-free inertial navigation units, ballistic platform guidance, and platform control.
Paradoxically Sparse Chemosensory Tuning in Broadly Integrating External Granule Cells in the Mouse Accessory Olfactory Bulb

The accessory olfactory bulb (AOB), the first neural circuit in the mouse accessory olfactory system, is critical for interpreting social chemosignals. Despite its importance, AOB information processing is poorly understood compared with the main olfactory bulb (MOB). Here, we sought to fill gaps in the understanding of AOB interneuron function. We used 2-photon GCaMP6f Ca2+ imaging in an ex vivo preparation to study chemosensory tuning in AOB external granule cells (EGCs), interneurons hypothesized to broadly inhibit activity in excitatory mitral cells (MCs). In ex vivo preparations from mice of both sexes, we measured MC and EGC tuning to natural chemosignal blends and monomolecular ligands, finding that EGC tuning was sparser, not broader, than upstream MCs. Simultaneous electrophysiological recording and Ca2+ imaging showed no differences in GCaMP6f-to-spiking relationships in these cell types during simulated sensory stimulation, suggesting that measured EGC sparseness was not due to cell type-dependent variability in GCaMP6f performance. Ex vivo patch-clamp recordings revealed that EGC subthreshold responsivity was far broader than indicated by GCaMP6f Ca2+ imaging, and that monomolecular ligands rarely elicited EGC spiking. These results indicate that EGCs are selectively engaged by chemosensory blends, suggesting different roles for EGCs than analogous interneurons in the MOB.SIGNIFICANCE STATEMENT The mouse accessory olfactory system (AOS) interprets social chemosignals, but we poorly understand AOS information processing. Here, we investigate the functional properties of external granule cells (EGCs), a major class of interneurons in the accessory olfactory bulb (AOB). We hypothesized that EGCs, which are densely innervated by excitatory mitral cells (MCs), would show broad chemosensory tuning, suggesting a role in divisive normalization. Using ex vivo GCaMP6f imaging, we found that EGCs were instead more sparsely tuned than MCs. This was not due to weaker GCaMP6f signaling in EGCs than in MCs. Instead, we found that many MC-activating chemosignals caused only subthreshold EGC responses. This indicates a different role for AOB EGCs compared with analogous cells in the main olfactory bulb.
On conditional versus marginal bias in multi-armed bandits

The bias of the sample means of the arms in multi-armed bandits is an important issue in adaptive data analysis that has recently received considerable attention in the literature. Existing results relate in precise ways the sign and magnitude of the bias to various sources of data adaptivity, but do not apply to the conditional inference setting in which the sample means are computed only if some specific conditions are satisfied. In this paper, we characterize the sign of the conditional bias of monotone functions of the rewards, including the sample mean. Our results hold for arbitrary conditioning events and leverage natural monotonicity properties of the data collection policy. We further demonstrate, through several examples from sequential testing and best arm identification, that the sign of the conditional and marginal bias of the sample mean of an arm can be different, depending on the conditioning event. Our analysis offers new and interesting perspectives on the subtleties of assessing the bias in data adaptive settings.
On the Borel submonoid of a symplectic monoid

Abstract In this article, we study the Bruhat-Chevalley-Renner order on the complex symplectic monoid M S p n . After showing that this order is completely determined by the Bruhat-Chevalley-Renner order on the linear algebraic monoid of n × n matrices M n , we focus on the Borel submonoid of M S p n . By using this submonoid, we introduce a new set of type B set partitions. We determine their count by using the “folding” and “unfolding” operators that we introduce. We show that the Borel submonoid of a rationally smooth reductive monoid with zero is rationally smooth. Finally, we analyze the nilpotent subsemigroups of the Borel semigroups of M n and M S p n . We show that, contrary to the case of M S p n , the nilpotent subsemigroup of the Borel submonoid of M n is irreducible.
Dense-Caption Matching and Frame-Selection Gating for Temporal Localization in VideoQA.

Videos convey rich information. Dynamic spatio-temporal relationships between people/objects, and diverse multimodal events are present in a video clip. Hence, it is important to develop automated models that can accurately extract such information from videos. Answering questions on videos is one of the tasks which can evaluate such AI abilities. In this paper, we propose a video question answering model which effectively integrates multi-modal input sources and finds the temporally relevant information to answer questions. Specifically, we first employ dense image captions to help identify objects and their detailed salient regions and actions, and hence give the model useful extra information (in explicit textual format to allow easier matching) for answering questions. Moreover, our model is also comprised of dual-level attention (word/object and frame level), multi-head self/cross-integration for different sources (video and dense captions), and gates which pass more relevant information to the classifier. Finally, we also cast the frame selection problem as a multi-label classification task and introduce two loss functions, In-andOut Frame Score Margin (IOFSM) and Balanced Binary Cross-Entropy (BBCE), to better supervise the model with human importance annotations. We evaluate our model on the challenging TVQA dataset, where each of our model components provides significant gains, and our overall model outperforms the state-of-the-art by a large margin (74.09% versus 70.52%). We also present several word, object, and frame level visualization studies.
Google Landmarks Dataset v2 – A Large-Scale Benchmark for Instance-Level Recognition and Retrieval

While image retrieval and instance recognition techniques are progressing rapidly, there is a need for challenging datasets to accurately measure their performance -- while posing novel challenges that are relevant for practical applications. We introduce the Google Landmarks Dataset v2 (GLDv2), a new benchmark for large-scale, fine-grained instance recognition and image retrieval in the domain of human-made and natural landmarks. GLDv2 is the largest such dataset to date by a large margin, including over 5M images and 200k distinct instance labels. Its test set consists of 118k images with ground truth annotations for both the retrieval and recognition tasks. The ground truth construction involved over 800 hours of human annotator work. Our new dataset has several challenging properties inspired by real-world applications that previous datasets did not consider: An extremely long-tailed class distribution, a large fraction of out-of-domain test photos and large intra-class variability. The dataset is sourced from Wikimedia Commons, the world's largest crowdsourced collection of landmark photos. We provide baseline results for both recognition and retrieval tasks based on state-of-the-art methods as well as competitive results from a public challenge. We further demonstrate the suitability of the dataset for transfer learning by showing that image embeddings trained on it achieve competitive retrieval performance on independent datasets. The dataset images, ground-truth and metric scoring code are available at https://github.com/cvdfoundation/google-landmark
Butterfly Transform: An Efficient FFT Based Neural Architecture Design

In this paper, we show that extending the butterfly operations from the FFT algorithm to a general Butterfly Transform (BFT) can be beneficial in building an efficient block structure for CNN designs. Pointwise convolutions, which we refer to as channel fusions, are the main computational bottleneck in the state-of-the-art efficient CNNs (e.g. MobileNets). We introduce a set of criterion for channel fusion, and prove that BFT yields an asymptotically optimal FLOP count with respect to these criteria. By replacing pointwise convolutions with BFT, we reduce the computational complexity of these layers from O(n^2) to O(n log n) with respect to the number of channels. Our experimental evaluations show that our method results in significant accuracy gains across a wide range of network architectures, especially at low FLOP ranges. For example, BFT results in up to a 6.75% absolute Top-1 improvement for MobileNetV1, 4.4 % for ShuffleNet V2 and 5.4% for MobileNetV3 on ImageNet under a similar number of FLOPS. Notably, ShuffleNet-V2+BFT outperforms state-of-the-art architecture search methods MNasNet, FBNet and MobilenetV3 in the low FLOP regime.