cssi/SciSciGPT-SciSciNet huggingface dataset:
Maize Yield and Nitrate Loss Prediction with Machine Learning Algorithms

Pre-growing season prediction of crop production outcomes such as grain yields and nitrogen (N) losses can provide insights to farmers and agronomists to make decisions. Simulation crop models can assist in scenario planning, but their use is limited because of data requirements and long runtimes. Thus, there is a need for more computationally expedient approaches to scale up predictions. We evaluated the potential of four machine learning (ML) algorithms (LASSO Regression, Ridge Regression, random forests, Extreme Gradient Boosting, and their ensembles) as meta-models for a cropping systems simulator (APSIM) to inform future decision support tool development. We asked: (1) How well do ML meta-models predict maize yield and N losses using pre-season information? (2) How many data are needed to train ML algorithms to achieve acceptable predictions? (3) Which input data variables are most important for accurate prediction? And (4) do ensembles of ML meta-models improve prediction? The simulated dataset included more than three million data including genotype, environment and management scenarios. XGBoost was the most accurate ML model in predicting yields with a relative mean square error (RRMSE) of 13.5%, and Random forests most accurately predicted N loss at planting time, with a RRMSE of 54%. ML meta-models reasonably reproduced simulated maize yields using the information available at planting, but not N loss. They also differed in their sensitivities to the size of the training dataset. Across all ML models, yield prediction error decreased by 10%–40% as the training dataset increased from 0.5 to 1.8 million data points, whereas N loss prediction error showed no consistent pattern. ML models also differed in their sensitivities to input variables (weather, soil properties, management, initial conditions), thus depending on the data availability researchers may use a different ML model. Modest prediction improvements resulted from ML ensembles. These results can help accelerate progress in coupling simulation models and ML toward developing dynamic decision support tools for pre-season management.
B-SIDH: Supersingular Isogeny Diffie-Hellman Using Twisted Torsion

This paper explores a new way of instantiating isogeny-based cryptography in which parties can work in both the \((p+1)\)-torsion of a set of supersingular curves and in the \((p-1)\)-torsion corresponding to the set of their quadratic twists. Although the isomorphism between a given supersingular curve and its quadratic twist is not defined over \(\mathbb {F}_{p^2}\) in general, restricting operations to the x-lines of both sets of twists allows all arithmetic to be carried out over \(\mathbb {F}_{p^2}\) as usual. Furthermore, since supersingular twists always have the same \(\mathbb {F}_{p^2}\)-rational j-invariant, the SIDH protocol remains unchanged when Alice and Bob are free to work in both sets of twists.
Injective State-Image Mapping facilitates Visual Adversarial Imitation Learning

The growing use of virtual autonomous agents in applications like games and entertainment demands better control policies for natural-looking movements and actions. Unlike the conventional approach of hard-coding motion routines, we propose a deep learning method for obtaining control policies by directly mimicking raw video demonstrations. Previous methods in this domain rely on extracting low-dimensional features from expert videos followed by a separate hand-crafted reward estimation step. We propose an imitation learning framework that reduces the dependence on hand-engineered reward functions by jointly learning the feature extraction and reward estimation steps using Generative Adversarial Networks (GANs). Our main contribution in this paper is to show that under injective mapping between low-level joint state (angles and velocities) trajectories and corresponding raw video stream, performing adversarial imitation learning on video demonstrations is equivalent to learning from the state trajectories. Experimental results show that the proposed adversarial learning method from raw videos produces a similar performance to state-of-the-art imitation learning techniques while frequently outperforming existing hand-crafted video imitation methods. Furthermore, we show that our method can learn action policies by imitating video demonstrations on YouTube with similar performance to learned agents from true reward signal. Please see the supplementary video submission at https://ibm.biz/BdzzNA.
Bit repetitive trellis coded modulation for the Rayleigh fading channel

A new approach to the problem of bandwidth efficient coding for transmission over the Rayleigh fading channel is considered. The proposed scheme is based on the idea of trellis coded modulation, but supports the re-transmission of 50% of the sources information. This re-transmission of data provides an additional level of time diversity, and allows for the identification of a new distortion criterion which may be used along with the squared Euclidean distance, to find the maximum likelihood path through the trellis. This new approach is developed utilizing estate trellis codes, along with two and four dimensional constellations, to support an information throughput of 2 bits/T. We will work through the design considerations applicable to this scheme, and provide performance comparisons between the proposed system, and TCM systems of comparable complexity. This new scheme achieves significant coding gains for bit error rates of practical interest. However, its complexity is about twice that of ordinary TCM.
Structural organization of the C1a-e-c supercomplex within the ciliary central apparatus

Nearly all motile cilia contain a central apparatus (CA) composed of two connected singlet microtubules with attached projections that play crucial roles in regulating ciliary motility. Defects in CA assembly usually result in motility-impaired or paralyzed cilia, which in humans causes disease. Despite their importance, the protein composition and functions of the CA projections are largely unknown. Here, we integrated biochemical and genetic approaches with cryo-electron tomography to compare the CA of wild-type Chlamydomonas with CA mutants. We identified a large (>2 MD) complex, the C1a-e-c supercomplex, that requires the PF16 protein for assembly and contains the CA components FAP76, FAP81, FAP92, and FAP216. We localized these subunits within the supercomplex using nanogold labeling and show that loss of any one of them results in impaired ciliary motility. These data provide insight into the subunit organization and 3D structure of the CA, which is a prerequisite for understanding the molecular mechanisms by which the CA regulates ciliary beating.
Sclerostin Antibody Treatment Increases Bone Mass and Normalizes Circulating Phosphate Levels in Growing Hyp Mice

X-linked hypophosphatemia (XLH), caused by a loss-of-function mutation in the phosphate regulating gene with homology to endopeptidase located on the X chromosome (PHEX), is the most common form of vitamin D-resistant rickets. Loss of functional PHEX results in elevated fibroblast growth factor 23 (FGF23) levels, impaired phosphate reabsorption, and inhibited skeletal mineralization. Sclerostin, a protein produced primarily in osteocytes, suppresses bone formation by antagonizing Wnt signaling and is reported to be elevated in XLH patients. This study used the Hyp mouse model to investigate sclerostin's role in the pathophysiology of XLH by evaluating the use of a monoclonal antibody to sclerostin in a mouse model of XLH, the Hyp mouse. Male and female wild-type and Hyp littermates were injected with 25 mg/kg of vehicle or sclerostin antibody (Scl-Ab) twice weekly, beginning at 4 weeks of age and euthanized at 8 weeks of age. Scl-Ab treatment increased serum phosphate levels and suppressed circulating levels of intact FGF23 in treated wild-type and Hyp mice of both sexes. Cortical area, trabecular bone volume fraction (BV/TV), metaphyseal apparent density, and the peak load increased with Scl-Ab treatment in both sexes. This short-term treatment study suggests that Scl-Ab treatment can effectively improve some of the pathologies associated with XLH, including normalization of phosphate, and that sclerostin may play a role in regulating FGF23 and phosphate metabolism in XLH. © 2019 American Society for Bone and Mineral Research.
Mechanoreceptor endings in human cervical facet joints

Twenty-one cervical facet capsules, taken from three normal human subjects, were examined to determine the type, density, and distribution of mechanoreceptive nerve endings in these tissues. Clearly identifiable mechanoreceptors were found in 17 of 21 specimens and were classified according to the scheme for encapsulated nerve endings established by Freeman and Wyke. Eleven Type I, 20 Type II, and 5 Type III receptors were identified, as well as a number of small, unencapsulated nerve endings. Type I receptors were small globular structures measuring 25-50 microns in diameter. Type II receptors varied in size and contour, but were characterized by their oblong shape and broad, lamellated capsule. Type III receptors were relatively large oblong structures with an amorphous capsule, within which a reticular meshwork of fine neurites was embedded. Free (nociceptive) nerve endings were found in subsynovial loose areolar and dense capsular tissues. The presence of mechanoreceptive and nociceptive nerve endings in cervical facet capsules proves that these tissues are monitored by the central nervous system and implies that neural input from the facets is important to proprioception and pain sensation in the cervical spine. Previous studies have suggested that protection muscular reflexes modulated by these types of mechanoreceptors are important in preventing joint instability and degeneration. It is suggested that the surgeon take steps to avoid inadvertently damaging these tissues when exposing the cervical spine.
Accurate annotation of human protein-coding small open reading frames

Functional protein-coding small open reading frames (smORFs) are emerging as an important class of genes. However, the number of translated smORFs in the human genome is unclear because proteogenomic methods are not sensitive enough, and, as we show, Ribo-seq strategies require additional measures to ensure comprehensive and accurate smORF annotation. Here, we integrate de novo transcriptome assembly and Ribo-seq into an improved workflow that overcomes obstacles with previous methods, to more confidently annotate thousands of smORFs. Evolutionary conservation analyses suggest that hundreds of smORF-encoded microproteins are likely functional. Additionally, many smORFs are regulated during fundamental biological processes, such as cell stress. Peptides derived from smORFs are also detectable on human leukocyte antigen complexes, revealing smORFs as a source of antigens. Thus, by including additional validation into our smORF annotation workflow, we accurately identify thousands of unannotated translated smORFs that will provide a rich pool of unexplored, functional human genes.
Wall jet similarity of impinging planar underexpanded jets

Abstract Velocity profiles and wall shear stress values in the wall jet region of planar underexpanded impinging jets are parameterized based on nozzle parameters (stand-off height, jet hydraulic diameter, and nozzle pressure ratio). Computational fluid dynamics is used to calculate the velocity fields of impinging jets with height-to-diameter ratios in the range of 15–30 and nozzle pressure ratio in the range of 1.2–3.0. The wall jet has an incomplete self-similar profile with a typical triple-layer structure as in traditional wall jets. The effects of compressibility are found to be insignificant for wall jets with Ma
Shareholders versus stakeholders in investor activism: Value for whom?

Abstract Is reduction in supplier costs a significant channel for value creation by activist investors? Using a comprehensive sample of shareholder activism during 1994–2015, and addressing endogeneity concerns, we find significant economics effects on the profits and capital investment of the target's suppliers after intervention. Consistent with the predictions of the incomplete contracting and bargaining literatures, factors that weaken (strengthen) the bargaining power of suppliers relative to targets amplify (dilute) the spillover effects on suppliers. The results are robust to the alternative hypotheses of latent industry effects on suppliers and stock picking by activist investors.
The Optimal Tariff, Production Lags, and Time Consistency

The optimal tariff for a large country equals the reciprocal of the foreign export elasticity of supply. However, if production decisions occur before consumption decisions, the ex ante optimal tariff is not time consistent because the ex post elasticity is less than the ex ante elasticity. We show all countries are worse off if the large country cannot precommit to its ex ante optimal tariff, and that all countries can gain if the large country taxes domestic production of importables.
Design and optimization of ellipsoid scatterer-based metasurfaces via the inverse T-matrix method

Large-area metasurfaces composed of discrete wavelength-scale scatterers present an extremely large number of degrees of freedom to engineer an optical element. While these degrees of freedom provide tremendous design flexibility, they also present a central challenge in metasurface design: how to optimally leverage these degrees of freedom towards a desired optical function. Inverse design is an attractive solution for this challenge. Here, we report an inverse design method exploiting T-matrix scattering of ellipsoidal scatterers. Multi-functional, polarization multiplexed metasurfaces were designed using this approach. We also optimized the efficiency of an existing high numerical aperture (0.83) metalens using the proposed method, and report an increase in efficiency from 26% to 32%.
Coupled structural transitions enable highly cooperative regulation of human CTPS2 filaments

Many enzymes assemble into defined oligomers, providing a mechanism for cooperatively regulating activity. Recent studies have described a mode of regulation in which enzyme activity is modulated by polymerization into large-scale filaments. Here we describe an ultrasensitive form of polymerization-based regulation employed by human CTP synthase 2 (CTPS2). Cryo-EM structures reveal that CTPS2 filaments dynamically switch between active and inactive forms in response to changes in substrate and product levels. Linking the conformational state of many CTPS2 subunits in a filament results in highly cooperative regulation, greatly exceeding the limits of cooperativity for the CTPS2 tetramer alone. The structures reveal a link between conformation and control of ammonia channeling between the enzyme's active sites, and explain differences in regulation of human CTPS isoforms. This filament-based mechanism of enhanced cooperativity demonstrates how the widespread phenomenon of enzyme polymerization can be adapted to achieve different regulatory outcomes.
Natural Language Generation at Scale: A Case Study for Open Domain Question Answering.

Current approaches to Natural Language Generation (NLG) for dialog mainly focus on domain-specific, task-oriented applications (e.g. restaurant booking) using limited ontologies (up to 20 slot types), usually without considering the previous conversation context. Furthermore, these approaches require large amounts of data for each domain, and do not benefit from examples that may be available for other domains. This work explores the feasibility of applying statistical NLG to scenarios requiring larger ontologies, such as multi-domain dialog applications or open-domain question answering (QA) based on knowledge graphs. We model NLG through an Encoder-Decoder framework using a large dataset of interactions between real-world users and a conversational agent for open-domain QA. First, we investigate the impact of increasing the number of slot types on the generation quality and experiment with different partitions of the QA data with progressively larger ontologies (up to 369 slot types). Second, we perform multi-task learning experiments between open-domain QA and task-oriented dialog, and benchmark our model on a popular NLG dataset. Moreover, we experiment with using the conversational context as an additional input to improve response generation quality. Our experiments show the feasibility of learning statistical NLG models for open-domain QA with larger ontologies.
TraceMontage: A method for merging multiple independent neuronal traces

Abstract Background The ability to reconstruct neuronal networks, local microcircuits, or the entire connectome is a central goal of modern neuroscience. Recently, advancements in sample preparation (e.g., sample expansion and Brainbow labeling) and optical (e.g., confocal and light sheet) techniques have enabled the imaging of increasingly large neural systems with high contrast. Tracing neuronal structures from these images proves challenging, however, necessitating tools that integrate multiple neuronal traces, potentially derived by various methods, into one combined (montaged) result. New method Here, we present TraceMontage, an ImageJ/Fiji plugin for the combination of multiple neuron traces of a single image, either redundantly or non-redundantly. Internally, it uses graph theory to connect topological patterns in the 3-D spatial coordinates of neuronal trees. The software generates a single output tracing file containing the montage traces of the input tracing files and provides several measures of consistency analysis among multiple tracers. Results and comparison to existing method(s) To our knowledge, our software is the first dedicated method for the combination of tracing results. Combining multiple tracers increases the accuracy and speed of tracing of densely-labeled samples by harnessing collaborative effort. This utility is demonstrated using fluorescence microscope images from the hippocampus and primary visual cortex (V1) in Brainbow-labeled mice. Conclusions TraceMontage provides researchers the ability to combine neuronal tracing data generated by either the same or different method(s). As datasets become larger, the ability to trace images in this parallel manner will help connectomics scale to increasingly larger neural systems.
Effect of chemoepitaxial guiding underlayer design on the pattern quality and shape of aligned lamellae for fabrication of line-space patterns

Chemoepitaxial guidance of block copolymer directed self-assembly in thin films is explored using a coarse-grained molecular dynamics model. The underlayers studied are 2× density multiplying line-space patterns composed of repeating highly preferential pinning stripes of various widths separated by larger, more neutral, background regions of various compositions. Decreasing the pinning stripe width or making the background region more neutral is found to increase the line edge roughness (LER) of the lines, but these conditions are found to give the straightest sidewalls for the formed lines. Varying these underlayer properties is found to have minimal effect on linewidth roughness. A larger pinning stripe causes the pinned line (PL) to foot (expand near the substrate), and a preferential background region causes the unpinned line (UPL) to undercut (contract near the substrate). A simple model was developed to predict the optimal conditions to eliminate footing. Using this model, conditions are found that decrease footing of the PL, but these conditions increase undercutting in the UPL. Deformations in either the PL or UPL propagate to the other line. There exists a trade-off between LER and the footing/undercutting, that is, decreasing LER increases footing/undercutting and vice versa.
Thermoacoustic Engines and Refrigerators

We ordinarily think of a sound wave in a gas as consisting of coupled pressure and displacement oscillations. However, temperature oscillations always accompany the pressure changes. The combination of all these oscillations, and their interaction with solid boundaries, produces a rich variety of “thermoacoustic” effects. Although these effects as they occur in everyday life are too small to be noticed, one can harness extremely loud sound waves in acoustically sealed chambers to produce powerful heat engines, heat pumps and refrigerators. Whereas typical engines and refrigerators have crankshaft‐coupled pistons or rotating turbines, thermoacoustic engines and refrigerators have at most a single flexing moving part (as in a loudspeaker) with no sliding seals. Thermoacoustic devices may be of practical use where simplicity, reliability or low cost is more important than the highest efficiency (although one cannot say much more about their cost‐competitiveness at this early stage).
Ricci curvature for parametric statistics via optimal transport

We define the notion of a Ricci curvature lower bound for parametrized statistical models. Following the seminal ideas of Lott–Sturm–Villani, we define this notion based on the geodesic convexity of the Kullback–Leibler divergence in a Wasserstein statistical manifold, that is, a manifold of probability distributions endowed with a Wasserstein metric tensor structure. Within these definitions, which are based on Fisher information matrix and Wasserstein Christoffel symbols, the Ricci curvature is related to both, information geometry and Wasserstein geometry. These definitions allow us to formulate bounds on the convergence rate of Wasserstein gradient flows and information functional inequalities in parameter space. We discuss examples of Ricci curvature lower bounds and convergence rates in exponential family models.
No strong evidence that social network index is associated with gray matter volume from a data-driven investigation.

Recent studies in adult humans have reported correlations between individual differences in people’s Social Network Index (SNI) and gray matter volume (GMV) across multiple regions of the brain. However, the cortical and subcortical loci identified are inconsistent across studies. These discrepancies might arise because different regions of interest were hypothesized and tested in different studies without controlling for multiple comparisons, and/or from insufficiently large sample sizes to fully protect against statistically unreliable findings. Here we took a data-driven approach in a pre-registered study to comprehensively investigate the relationship between SNI and GMV in every cortical and subcortical region, using three predictive modeling frameworks. We also included psychological predictors such as cognitive and emotional intelligence, personality, and mood. In a sample of healthy adults (n = 92), neither multivariate frameworks (e.g., ridge regression with cross-validation) nor univariate frameworks (e.g., univariate linear regression with cross-validation) showed a significant association between SNI and any GMV or psychological feature after multiple comparison corrections (all R-squared values ≤ 0.1). These results emphasize the importance of large sample sizes and hypothesis-driven studies to derive statistically reliable conclusions, and suggest that future meta-analyses will be needed to more accurately estimate the true effect sizes in this field.
A minimally invasive technique for serial intraosseous perfusion measurements in the murine tibia using laser Doppler flowmetry.

ABSTRACT In biomedical and preclinical research, the current standard method for measuring blood perfusion inside murine bone, radiolabeled microspheres, is a terminal procedure that cannot be used to monitor longitudinal perfusion changes. Laser Doppler flowmetry (LDF) can assess perfusion within the proximal tibial metaphysis of mice in vivo but requires a surgical procedure to place the measurement probe directly onto the bone surface. Sustained inflammation for over a month following this technique was previously reported, and previous studies have only used LDF as an endpoint-only procedure. We developed a modified, minimally invasive LDF procedure to measure intraosseous perfusion in the murine tibia without stimulating local or systemic inflammation or inducing gait abnormalities. This modified technique can be used to measure perfusion weekly for up to at least a month in the murine tibia. • Unlike previous endpoint-only techniques, this modified LDF procedure can be performed weekly to monitor serial changes to intraosseous perfusion in the murine tibia • The modified LDF technique utilizes a smaller, more localized incision to minimize invasiveness and speed recovery
Estimates Near the Boundary for Critical SQG

We obtain estimates near the boundary for the critical dissipative SQG equation in bounded domains, with the square root of the Dirichlet Laplacian dissipation. We prove that global regularity up to the boundary holds if and only if a certain quantitative vanishing of the scalar at the boundary is maintained.
Isogeometric collocation method for the fractional Laplacian in the 2D bounded domain

Abstract We consider the isogeometric analysis for fractional PDEs involving the fractional Laplacian in two dimensions. An isogeometric collocation method is developed to discretize the fractional Laplacian and applied to the fractional Poisson problem and the time-dependent fractional porous media equation. Numerical studies exhibit monotonous convergence with a rate of O ( N − 1 ) , where N is the degrees of freedom. A comparison with finite element analysis shows that the method enjoys higher accuracy per degree of freedom and has better convergence rate. We demonstrate that isogeometric analysis offers a novel and promising computational tool for nonlocal problems.
Higher Ground? How Groundtruth Labeling Impacts Our Understanding of Fake News about the 2016 U.S. Presidential Nominees

The spread of fake news on social media platforms has garnered much public attention and apprehension. Consequently, both the tech industry and academia alike are investing increased effort to understand, detect, and curb fake news. Yet, researchers differ in what they consider to be fake news sites. In this paper, we first aggregate 5 lists of fake and 3 of mainstream news sites published by experts and reputable organizations. Then, focusing on tweets about the democratic (Hillary Clinton) and republican (Donald Trump) nominees in the 2016 U.S. presidential election, we use each pair of fake and traditional news lists as an independent “groundtruth” to examine i) the prevalence, ii) temporal characteristics and iii) the agenda-setting differences between fake and traditional news sites. We observe that depending on the groundtruth, the prevalence of fake news varies significantly. However, the temporal trends and agenda-setting differences between fake and mainstream news sites remain moderately consistent across different groundtruth lists.
Would you Like to Talk about Sports Now?: Towards Contextual Topic Suggestion for Open-Domain Conversational Agents

To hold a true conversation, an intelligent agent should be able to occasionally take initiative and recommend the next natural conversation topic. This is a challenging task. A topic suggested by the agent should be relevant to the person, appropriate for the conversation context, and the agent should have something interesting to say about it. Thus, a scripted, or one-size-fits-all, popularity-based topic suggestion is doomed to fail. Instead, we explore different methods for a personalized, contextual topic suggestion for open-domain conversations. We formalize the Conversational Topic Suggestion problem (CTS) to more clearly identify the assumptions and requirements. We also explore three possible approaches to solve this problem: (1) model-based sequential topic suggestion to capture the conversation context (CTS-Seq), (2) Collaborative Filtering-based suggestion to capture previous successful conversations from similar users (CTS-CF), and (3) a hybrid approach combining both conversation context and collaborative filtering. To evaluate the effectiveness of these methods, we use real conversations collected as part of the Amazon Alexa Prize 2018 Conversational AI challenge. The results are promising: the CTS-Seq model suggests topics with 23% higher accuracy than the baseline, and incorporating collaborative filtering signals into a hybrid CTS-Seq-CF model further improves recommendation accuracy by 12%. Together, our proposed models, experiments, and analysis significantly advance the study of open-domain conversational agents, and suggest promising directions for future improvements.
A Predictive Deep Learning Approach to Output Regulation: The Case of Collaborative Pursuit Evasion

In this paper, we consider the problem of controlling an underactuated system in unknown, and potentially adversarial environments. The emphasis will be on autonomous aerial vehicles, modelled by Dubins dynamics. The proposed control law is based on a variable integrator via online prediction for target tracking. To showcase its efficacy we analyze a pursuit evasion game between multiple autonomous agents. To obviate the need for perfect knowledge of the evader's future strategy, we use a deep neural network that is trained to approximate the behavior of the evader based on measurements gathered online during the pursuit.
Graphite Lithiation under Fast Charging Conditions: Atomistic Modeling Insights

Charging lithium ion batteries in a fast and safe manner is critical for promoting mass adoption of electric vehicles. Li intercalation in graphite electrodes is known to be one of the bottlenecks ...
Bounded and divergent trajectories and expanding curves on homogeneous spaces

Suppose $g_t$ is a $1$-parameter $\mathrm{Ad}$-diagonalizable subgroup of a Lie group $G$ and $\Gamma < G$ is a lattice. We study the dimension of bounded and divergent orbits of $g_t$ emanating from a class of curves lying on leaves of the unstable foliation of $g_t$ on the homogeneous space $G/\Gamma$. We obtain sharp upper bounds on the Hausdorff dimension of divergent on average orbits and show that the set of bounded orbits is winning in the sense of Schmidt (and, hence, has full dimension). The class of curves we study is roughly characterized by being tangent to copies of $\mathrm{SL}(2,\mathbb{R})$ inside $G$, which are not contained in a proper parabolic subgroup of $G$. We describe applications of our results to problems in Diophantine approximation by number fields and intrinsic Diophantine approximation on spheres. Our methods also yield the following result for lines in the space of square systems of linear forms: suppose $\varphi(s) = sY + Z$ where $Y\in \mathrm{GL}(n,\mathbb{R})$ and $Z\in M_{n,n}(\mathbb{R})$. Then, the dimension of the set of points $s$ such that $\varphi(s)$ is singular is at most $1/2$ while badly approximable points have Hausdorff dimension equal to $1$.
Complex Transformer: A Framework for Modeling Complex-Valued Sequence

While deep learning has received a surge of interest in a variety of fields in recent years, major deep learning models barely use complex numbers. However, speech, signal and audio data are naturally complex-valued after Fourier Transform, and studies have shown a potentially richer representation of complex nets. In this paper, we propose a Complex Transformer, which incorporates the transformer model as a backbone for sequence modeling; we also develop attention and encoder-decoder network operating for complex input. The model achieves state-of-the-art performance on the MusicNet dataset and an In-phase Quadrature (IQ) signal dataset. The GitHub implementation to reproduce the experimental results is available at https://github.com/muqiaoy/dl_signal.
DolphinNext: a distributed data processing platform for high throughput genomics

The emergence of high throughput technologies that produce vast amounts of genomic data, such as next-generation sequencing (NGS) is transforming biological research. The dramatic increase in the volume of data, the variety and continuous change of data processing tools, algorithms and databases make analysis the main bottleneck for scientific discovery. The processing of high throughput datasets typically involves many different computational programs, each of which performs a specific step in a pipeline. Given the wide range of applications and organizational infrastructures, there is a great need for highly parallel, flexible, portable, and reproducible data processing frameworks. Several platforms currently exist for the design and execution of complex pipelines. Unfortunately, current platforms lack the necessary combination of parallelism, portability, flexibility and/or reproducibility that are required by the current research environment. To address these shortcomings, workflow frameworks that provide a platform to develop and share portable pipelines have recently arisen. We complement these new platforms by providing a graphical user interface to create, maintain, and execute complex pipelines. Such a platform will simplify robust and reproducible workflow creation for non-technical users as well as provide a robust platform to maintain pipelines for large organizations. To simplify development, maintenance, and execution of complex pipelines we created DolphinNext. DolphinNext facilitates building and deployment of complex pipelines using a modular approach implemented in a graphical interface that relies on the powerful Nextflow workflow framework by providing 1. A drag and drop user interface that visualizes pipelines and allows users to create pipelines without familiarity in underlying programming languages. 2. Modules to execute and monitor pipelines in distributed computing environments such as high-performance clusters and/or cloud 3. Reproducible pipelines with version tracking and stand-alone versions that can be run independently. 4. Modular process design with process revisioning support to increase reusability and pipeline development efficiency. 5. Pipeline sharing with GitHub and automated testing 6. Extensive reports with R-markdown and shiny support for interactive data visualization and analysis. DolphinNext is a flexible, intuitive, web-based data processing and analysis platform that enables creating, deploying, sharing, and executing complex Nextflow pipelines with extensive revisioning and interactive reporting to enhance reproducible results.
SpArch: Efficient Architecture for Sparse Matrix Multiplication

Generalized Sparse Matrix-Matrix Multiplication (SpGEMM) is a ubiquitous task in various engineering and scientific applications. However, inner product based SpGEMM introduces redundant input fetches for mismatched nonzero operands, while outer product based approach suffers from poor output locality due to numerous partial product matrices. Inefficiency in the reuse of either inputs or outputs data leads to extensive and expensive DRAM access. To address this problem, this paper proposes an efficient sparse matrix multiplication accelerator architecture, SpArch, which jointly optimizes the data locality for both input and output matrices. We first design a highly parallelized streaming-based merger to pipeline the multiply and merge stage of partial matrices so that partial matrices are merged on chip immediately after produced. We then propose a condensed matrix representation that reduces the number of partial matrices by three orders of magnitude and thus reduces DRAM access by 5.4x. We further develop a Huffman tree scheduler to improve the scalability of the merger for larger sparse matrices, which reduces the DRAM access by another 1.8x. We also resolve the increased input matrix read induced by the new representation using a row prefetcher with near-optimal buffer replacement policy, further reducing the DRAM access by 1.5x. Evaluated on 20 benchmarks, SpArch reduces the total DRAM access by 2.8x over previous state-of-the-art. On average, SpArch achieves 4x, 19x, 18x, 17x, 1285x speedup and 6x, 164x, 435x, 307x, 62x energy savings over OuterSpace, MKL, cuSPARSE, CUSP, and ARM Armadillo, respectively.
Log Transformation Improves Dating of Phylogenies

The level of divergence between species represented by sequence data is a function of unknown time and mutation rates. Therefore, sequence data do not reveal exact timing of evolutionary events, and inferred phylogenies often have branch lengths estimated in the unit of the expected number of substitutions. Dating a phylogeny is the process of translating branch lengths from this unit to time unit. Such a process requires soft or hard constraints for the timing of some nodes and infers the divergence times of the remaining nodes.
A knowledge-based decision support architecture for advanced traffic management

Abstract Fundamental to the operation of most currently envisioned Intelligent Vehicle-Roadway System (IVRS) projects are advanced systems for surveillance, control and management of integrated freeway and arterial networks. A major concern in the development of such Smart Roads, and the focus of this paper, is the provision of decision support for traffic management center personnel, particularly for addressing nonrecurring congestion in large or complex networks. Decision support for control room staff is necessary to effectively detect, verify and develop response strategies for traffic incidents. The purpose of this paper is to suggest a novel artificial intelligence-based solution approach to the problem of providing operator decision support in integrated freeway and arterial traffic management systems, as part of a more general IVRS. A conceptual design is presented that is based on multiple real-time knowledge-based expert systems (KBES) integrated by a distributed blackboard problem-solving architecture. The paper expands on the notions of artificial intelligence and Smart Roads, and in particular the role, characteristics and requirements of KBES for real-time decision support. The overall concept of a decision support architecture is discussed and the blackboard approach is defined. A conceptual design for the proposed distributed blackboard architecture is presented, and discussed in terms of the component KBES functions at an areawide level, as well as the subnetwork or individual traffic control center level.
Computational and experimental study of turbulent flows in 180 degree bends

A theoretical and experimental investigation of turbulent flows inside a 180-deg strongly curved channel is included in the present study. A 10 by 100 centimeter water channel with a 10 centimeter radius turn was employed for the experimental study. Data measured are velocity profiles and distributions of longitudinal turbulence intensity. Numerical investigations are performed by solving the incompressible Navier-Stokes equations and turbulence models using a finite difference flow solver in generalized curvilinear coordinate systems. A widely used two-equation turbulence model and its modified version, which includes the effect of strong curvature on the turbulence length scale through a relation of algebraic Reynolds stress model, are employed to predict the turbulence quantities inside the flow field. Improvements on the predictions are obtained using the present approach.
Effects of guanidine derivatives on mitochondrial function

Steady-state concentrations of citric acid cycle compounds accumulating during state 3 oxidation of pyruvate by guinea pig heart mitochondria have been measured using isotopic and fluorometric techniques; incubations partially inhibited with several guanidine derivatives and other inhibitors were compared with controls. The changes in levels of intermediates which occurred with guanidine derivatives were quantitatively and qualitatively identical to those with amytal; this pattern of intermediates was therefore not limited to those compounds which possess hypoglycemic propertiesin vitro. With antimycin, rotenone and nigericin the pattern of intermediates was specific for each agent, and differed from that with guanidine derivatives and amytal. Reduced pyridine nucleotides were also estimated at progressively increasing degrees of respiratory inhibition by these same agents. Lower concentrations of phenethylbiguanide and amytal produced identical increases in state 3 level of reduced pyridine nucleotide, while higher phenethylbiguanide concentrations were associated with a phosphatedependent decrease in reduced pyridine nucleotide level in both state 3 and state 4 which was not observed with amytal, and not accompanied by a stimulation of respiratory rate. These changes resemble the metabolic condition termed state 6, and are consistent with a calciumlike activity of guanidine derivatives. Changes in level of reduced pyridine nucleotide were observed which were specific to rotenone and nigericin; these changes, combined with the patterns of citric acid cycle intermediates observed with these inhibitors, are useful in interpreting the effects of these agents on the functional state of intact mitochondria.
Outbreaks of Stem and Leaf Blight of Eustoma grandiflorum Caused by a Phomopsis sp. in Florida

Between November 1997 and May 1998, numerous lisianthus (Eustoma grandiflorum) cultivars exhibited severe stem and leaf blight at two pot-flower production sites in Hillsborough and Dade counties, FL
Government Advertising and Media Coverage of Corruption Scandals

We construct measures of the extent to which the four main newspapers in Argentina report government corruption on their front page during the period 1998–2007 and correlate them with government advertising. The correlation is negative. The size is considerable—a one standard deviation increase in monthly government advertising is associated with a reduction in the coverage of the government’s cor ruption scandals of 0.23 of a front page per month, or 18 percent of a standard deviation in coverage. The results are robust to the inclusion of newspaper, month, newspaper × president and individualcorruption scandal fixed effects, as well as newspaper × president specific time trends. (JEL D72, K42, L82, M37, O17)
Digitally Equalized CMOS Transmitter Front-End With Integrated Power Amplifier

An energy-efficient, 3.5-GHz, direct-conversion RF transmitter with integrated 23-dBm, Class-B power amplifier (PA) is fabricated in a 0.13-μm CMOS process for the reliable transmission of 20-MHz bandwidth OFDM signals. Assisted by an integrated feedback path, a two-dimensional, digital look-up table (2-D LUT) adapted by complex gradient-descent algorithms compensates for the I/Q mismatch and memoryless nonlinearities of the whole transmit path, including the severe amplitude and phase distortions of the on-chip PA. Fifth-order Butterworth transmit filters with 34-MHz cutoff frequency sufficiently attenuate the aliases of the 80-MS/s digital-to-analog converters (DACs), while retaining high in-band fidelity without corrupting the predistorted signal. When a 20-MHz, 64-QAM OFDM signal with 9.6-dB peak-to-average power ratio (PAPR) was transmitted, the measured average drain efficiency (DE) of the PA was 12.5 % at 9.6-dB back-off and 17.5% at 7-dB back-off, and the corresponding error-vector magnitude (EVM) was measured to be —29.6 dB and -26.3 dB with equalization, respectively. A peak DE of 55 % and a 25-dBm saturated output power were also measured for the same PA in a stand-alone package.
Noetherian intersections of regular local rings of dimension two

Abstract Let D be a 2-dimensional regular local ring and let Q ( D ) denote the quadratic tree of 2-dimensional regular local overrings of D. We describe the desingularization of projective models over D both algebraically in terms of the saturation of complete ideals and order-theoretically in terms of the quadratic tree Q ( D ) . We prove that this describes all the Noetherian rings that are intersections of rings in Q ( D ) .
Pluronic Polyols—Cell Protection

Pluronic polyols, especially Pluronic F-68, are commonly added to cell culture media to protect insect and mammalian cells from the hydrodynamic stress resulting from sparging. This article describes the relevant properties of Pluronic polyols, the ability of Pluronic polyols to protect insect and mammalian cells from sparging and agitation, and the application of Pluronic polyols to cell culture. Keywords: cell culture scale-up; pluronic polyols; sparing protection
Preference heterogeneity and optimal capital income taxation

article i nfo We examine a prominent justification for capital income taxation: goods preferred by those with high ability ought to be taxed. In an environment where commodity taxes are allowed to be nonlinear functions of income and consumption, we derive an analytical expression that reveals the forces determining optimal commodity taxation. We then calibrate the model to evidence on the relationship between skills and prefer- ences and extensively examine the quantitative case for taxes on future consumption (saving). In our base- line case of a unit intertemporal elasticity, optimal capital income tax rates are 2% on average and 4.5% on high earners. We find that the intertemporal elasticity of substitution has a substantial effect on optimal cap- ital taxation. If the intertemporal elasticity is one-third, the optimal capital income tax rates rise to 15% on average and 23% on high earners; if the intertemporal elasticity is two, the optimal rates fall to 0.6% on aver- age and 1.6% on high earners. Nevertheless, in all cases that we consider the welfare gains of using optimal capital taxes are small.
StanDep: Capturing transcriptomic variability improves context-specific metabolic models

Diverse algorithms can integrate transcriptomics with genome-scale metabolic models (GEMs) to build context-specific metabolic models. These algorithms require identification of a list of high confidence (core) reactions from transcriptomics, but parameters related to identification of core reactions, such as thresholding of expression profiles, can significantly change model content. Importantly, current thresholding approaches are burdened with setting singular arbitrary thresholds for all genes; thus, resulting in removal of enzymes needed in small amounts and even many housekeeping genes. Here, we describe StanDep, a novel heuristic method for using transcriptomics to identify core reactions prior to building context-specific metabolic models. StanDep clusters gene expression data based on their expression pattern across different contexts and determines thresholds for each cluster using data-dependent statistics, specifically standard deviation and mean. To demonstrate the use of StanDep, we built hundreds of models for the NCI-60 cancer cell lines. These models successfully increased the inclusion of housekeeping reactions, which are often lost in models built using standard thresholding approaches. Further, StanDep also provided a transcriptomic explanation for inclusion of lowly expressed reactions that were otherwise only supported by model extraction methods. Our study also provides novel insights into how cells may deal with context-specific and ubiquitous functions. StanDep, as a MATLAB toolbox, is available at https://github.com/LewisLabUCSD/StanDep.