cssi/SciSciGPT-SciSciNet huggingface dataset:
A new reagent for incorporating the 2-(1-adamantyl)-2-propyloxycarbonyl protecting group into amines

A new reagent to incorporate the 2-(1-adamantyl)-2-propyloxycarbonyl (Ad-POC) protecting group into amines, 2-(1-adamantyl)-2-propyl p-nitrophenylcarbonate, was prepared in two steps from the commercially available 1-adamantylcarbonyl chloride. The Ad-POC group was introduced into a variety of amines and amino acid derivatives. © 2000 John Wiley & Sons, Inc. Heteroatom Chem 11:192–195, 2000
Pervasive and Dynamic Transcription Initiation in Saccharomyces cerevisiae

Transcription initiation is finely regulated to ensure proper expression and function of genes. The regulated transcription initiation in response to various environmental stimuli in a classic model organism Saccharomyces cerevisiae has not been systematically investigated. In this study, we generated quantitative maps of transcription start sites (TSSs) at a single-nucleotide resolution for S. cerevisiae grown in nine different conditions using no-amplification nontagging Cap analysis of gene expression (nAnT-iCAGE) sequencing. We mapped ∼1 million well-supported TSSs, suggesting highly pervasive transcription initiation in the compact genome of the budding yeast. The comprehensive TSS maps allowed us to identify core promoters for ∼96% verified protein-coding genes. We corrected misannotation of translation start codon for 122 genes and suggested an alternative start codon for 57 genes. We found that 56% of yeast genes are controlled by multiple core promoters, and alternative core promoter usage by a gene is widespread in response to changing environments. Most core promoter shifts are coupled with altered gene expression, indicating that alternative core promoter usage might play an important role in controlling gene transcriptional activities. Based on their activities in responding to environmental cues, we divided core promoters into constitutive class (55%) and inducible class (45%). The two classes of core promoters display distinctive patterns in transcriptional abundance, chromatin structure, promoter shape, and sequence context. In summary, our study improved the annotation of the yeast genome and demonstrated a much more pervasive and dynamic nature of transcription initiation in yeast than previously recognized.
Reductive N–O cleavage of Weinreb amides by sodium in alumina and silica gels: synthetic and mechanistic studies

Abstract The use of sodium in alumina and silica gels for the reductive cleavage of the N–O bond of N-methoxy-N-methylamides, commonly referred to as Weinreb amides, has been investigated. This method reduces a diverse set of Weinreb amides with different functional groups to give modest to excellent yields. In the course of the studies to explore mechanisms and functional group tolerance, an apparently novel group transfer emerged, implying base-promoted cleavage of the Weinreb amide to form formaldehyde, followed by aldol condensation.
Enhanced Mesostructural Order and Changes to Optical and Electrochemical Properties Induced by the Addition of Cerium(III) to Mesoporous Titania Thin Films

The addition of up to 10 mol % cerium(III) during the synthesis of cubic mesoporous titania thin films significantly increases the degree of mesostructural order in the final material. Photocurrent measurements, cyclic voltammetry, and spectroelectrochemistry show that the addition of cerium introduces a large number of interfacial states into the mesoporous material, which act as electron traps. Electron trapping by individual cerium ions also causes changes in the electrochemical properties of the mesoporous films. The mesoporous cerium-doped titania films show an increased electrochromic response that is indicated by an increase in absorbance at 550 nm with respect to pure titania. This effect is counter to the behavior of sol−gel ceria−titania films, which generally show a loss of electrochromism compared to pure titania.
Some observations regarding the stereochemical course of iminium ion reductions: An example of the size difference between sodium cyanoborohydride and sodium triacetoxyborohydride

Abstract The stereochemical course of reductions of iminium ions with sodium cyanoborohydride and sodium triacetoxyborohydride was examined within the context of model studies directed toward the synthesis of the quinolizidine alkaloid clavepictine A. Conformational preferences of the iminium ion and and effective size of the reducing agent were shown play a role in determining reduction stereochemistry.
Modeling and control battery aging in energy harvesting systems

Energy storage is a fundamental component for the development of sustainable and environment-aware technologies. One of the critical challenges that needs to be overcome is preserving the State of Health (SoH) in energy harvesting systems, where bursty arrival of energy and load may severely degrade the battery. Tools from Markov process and Dynamic Programming theory are becoming an increasingly popular choice to control dynamics of these systems due to their ability to seamlessly incorporate heterogeneous components and support a wide range of applications. Mapping aging rate measures to fit within the boundaries of these tools is non-trivial. In this paper, a framework for modeling and controlling the aging rate of batteries based on Markov process theory is presented. Numerical results illustrate the tradeoff between battery degradation and task completion delay enabled by the proposed framework.
A nonparametric frequency domain EM algorithm for time series classification with applications to spike sorting and macro-economics

I propose a frequency domain adaptation of the Expectation Maximization algorithm to group a family of time series in classes of similar dynamic structure. It does this by viewing the magnitude of the discrete Fourier transform of each signal (or power spectrum) as a probability density/mass function (pdf/pmf) on the unit circle: signals with similar dynamics have similar pdfs; distinct patterns have distinct pdfs. An advantage of this approach is that it does not rely on any parametric form of the dynamic structure, but can be used for nonparametric, robust and model-free classification. This new method works for non-stationary signals of similar shape as well as stationary signals with similar auto-correlation structure. Applications to neural spike sorting (non-stationary) and pattern recognition in socioeconomic time series (stationary) demonstrate the usefulness and wide applicability of the proposed method. © 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 4: 590–603, 2011
Towards Robust Neural Networks via Random Self-ensemble

Recent studies have revealed the vulnerability of deep neural networks: A small adversarial perturbation that is imperceptible to human can easily make a well-trained deep neural network misclassify. This makes it unsafe to apply neural networks in security-critical applications. In this paper, we propose a new defense algorithm called Random Self-Ensemble (RSE) by combining two important concepts: randomness and ensemble. To protect a targeted model, RSE adds random noise layers to the neural network to prevent the strong gradient-based attacks, and ensembles the prediction over random noises to stabilize the performance. We show that our algorithm is equivalent to ensemble an infinite number of noisy models \(f_\epsilon \) without any additional memory overhead, and the proposed training procedure based on noisy stochastic gradient descent can ensure the ensemble model has a good predictive capability. Our algorithm significantly outperforms previous defense techniques on real data sets. For instance, on CIFAR-10 with VGG network (which has 92% accuracy without any attack), under the strong C&W attack within a certain distortion tolerance, the accuracy of unprotected model drops to less than 10%, the best previous defense technique has \(48\%\) accuracy, while our method still has \(86\%\) prediction accuracy under the same level of attack. Finally, our method is simple and easy to integrate into any neural network.
On the diagonalization of the discrete Fourier transform

Abstract The discrete Fourier transform (DFT) is an important operator which acts on the Hilbert space of complex valued functions on the ring Z / N Z . In the case where N = p is an odd prime number, we exhibit a canonical basis Φ of eigenvectors for the DFT. The transition matrix Θ from the standard basis to Φ defines a novel transform which we call the discrete oscillator transform (DOT for short). Finally, we describe a fast algorithm for computing Θ in certain cases.
Rigidity theorems on hemispheres in non-positive space forms

We study the curvature condition which uniquely characterizes the hemisphere. In particular, we prove the Min-Oo conjecture for hypersurfaces in Euclidean space and hyperbolic space.
Towards the Tradeoff Between Service Performance and Information Freshness

The last decade has witnessed an unprecedented growth in the demand for data-driven real-time services. These services are fueled by emerging applications that require rapidly injecting data streams and computing updated analytics results in real-time (or near-real-time). In many of such applications, the computing resources are often shared for processing both updates from information sources and queries from end users. This requires joint scheduling of updates and queries because the service provider needs to make a critical decision upon receiving a user query: either it responds immediately with currently available but possibly stale information, or it first processes new updates and then responds with fresher information. Hence, the tradeoff between service performance (e.g., response time) and information freshness naturally arises in this context. To that end, we propose a simple single-server two-queue model that captures the coupled scheduling of updates and queries and aim to design scheduling policies that can properly address the important tradeoff between performance and freshness. Specifically, we consider the response time as a performance metric and the Age of Information (AoI) as a freshness metric. After demonstrating the limitations of the simplest First-Come-First-Served (FCFS) policy, we propose two threshold-based policies: the Query-k policy that prioritizes queries and the Update-k policy that prioritizes updates. Then, we rigorously analyze both the response time and the Peak AoI (PAoI) of the threshold-based policies. Further, we propose the Joint-(M, N) policy, which allows flexibly prioritizing updates or queries through choosing different values of two thresholds M and N. Finally, we conduct simulations to evaluate the response time and the PAoI of the proposed policies. The results show that our proposed threshold-based policies can effectively control the balance between performance and freshness.
Connected components of complex divisor functions

Abstract Text For any complex number c, define the divisor function σ c : N → C by σ c ( n ) = ∑ d | n d c . Let σ c ( N ) ‾ denote the topological closure of the range of σ c . Extending previous work of the current author and Sanna, we prove that σ c ( N ) ‾ has nonempty interior and has finitely many connected components if ℜ ( c ) ≤ 0 and c ≠ 0 . We end with some open problems. Video For a video summary of this paper, please visit https://youtu.be/-Zic04j6sRg .
Driver Gaze Zone Estimation Using Convolutional Neural Networks: A General Framework and Ablative Analysis

Driver gaze has been shown to be an excellent surrogate for driver attention in intelligent vehicles. With the recent surge of highly autonomous vehicles, driver gaze can be useful for determining the handoff time to a human driver. While there has been significant improvement in personalized driver gaze zone estimation systems, a generalized system which is invariant to different subjects, perspectives, and scales is still lacking. We take a step toward this generalized system using convolutional neural networks (CNNs). We finetune four popular CNN architectures for this task, and provide extensive comparisons of their outputs. We additionally experiment with different input image patches, and also examine how the image size affects performance. For training and testing the networks, we collect a large naturalistic driving dataset comprising of 11 long drives, driven by ten subjects in two different cars. Our best performing model achieves an accuracy of 95.18% during cross-subject testing, outperforming current state-of-the-art techniques for this task. Finally, we evaluate our best performing model on the publicly available Columbia gaze dataset comprising of images from 56 subjects with varying head pose and gaze directions. Without any training, our model successfully encodes the different gaze directions on this diverse dataset, demonstrating good generalization capabilities.
Degrees of freedom in deep neural networks

In this paper, we explore degrees of freedom in deep sigmoidal neural networks. We show that the degrees of freedom in these models are related to the expected optimism, which is the expected difference between test error and training error. We provide an efficient Monte-Carlo method to estimate the degrees of freedom for multi-class classification methods. We show that the degrees of freedom is less than the parameter count in a simple XOR network. We extend these results to neural nets trained on synthetic and real data and investigate the impact of network's architecture and different regularization choices. The degrees of freedom in deep networks is dramatically less than the number of parameters. In some real datasets, the number of parameters is several orders of magnitude larger than the degrees of freedom. Further, we observe that for fixed number of parameters, deeper networks have less degrees of freedom exhibiting a regularization-by-depth. Finally, we show that the degrees of freedom of deep neural networks can be used in a model selection criterion. This criterion has comparable performance to cross-validation with lower computational cost.
Performance bounds for optimal feedback control in networks

Many important complex networks, including critical infrastructure and emerging industrial automation systems, are becoming increasingly intricate webs of interacting feedback control loops. A fundamental concern is to quantify the control properties and performance limitations of the network as a function of its dynamical structure and control architecture. We study performance bounds for networks in terms of optimal feedback control costs. We provide a set of complementary bounds as a function of the system dynamics and actuator structure. For unstable network dynamics, we characterize a tradeoff between feedback control performance and the number of control inputs, in particular showing that optimal cost can increase exponentially with the size of the network. We also derive a bound on the performance of the worst-case actuator subset for stable networks, providing insight into dynamics properties that affect the potential efficacy of actuator selection. We illustrate our results with numerical experiments that analyze performance in regular and random networks.
Inhomogeneous phases and chiral symmetry breaking

Abstract A significant fraction of the current efforts in the QCD research community is focused on characterizing the phases of strong-interaction matter that occur at finite densities and temperatures. So far, most of the experimental probes have been limited to the relatively narrow window of the QCD phase diagram characterized by high temperatures and low chemical potentials, explored in high-energy ion collision experiments at RHIC and LHC. More recently, some new insight on the finite chemical potential region has been obtained by the energy-beam scan program at RHIC, aimed at possibly determining the existence of a critical point in the QCD phase transition. On the theoretical side, studies of strong interactions are also limited by the reliability of available methods. While the zero density, finite temperature region or the zero temperature, superdense region can be investigated with the help of well-established approaches like lattice and weakly coupled QCD respectively, the study of the intermediate densities and temperatures region has to rely on effective models and nonperturbative methods, some of which are still being developed. In the past few years, a growing number of compelling arguments, backed up by model calculations, pointed out that the intermediate-density region of the QCD phase diagram may be characterized by the formation of inhomogeneous condensates which spontaneously break some of the spatial symmetries of the theory. In the following we provide a brief recapitulation of these arguments and describe some recent results in a 3+1-dimensional QCD-inspired NJL model with quark-hole condensation in the form of a plane wave in the scalar and tensor channels. This model exhibits particular features in close analogy to its 1+1-dimensional counterpart, most notably an asymmetric spectral density and the arising of an anomalous contribution to the free energy.
Reduced Bias for Respondent Driven Sampling: Accounting for Non-Uniform Edge Sampling Probabilities in People Who Inject Drugs in Mauritius

People who inject drugs are an important population to study to reduce transmission of blood‐borne illnesses including human immunodeficiency virus and hepatitis. We estimate the human immunodeficiency virus and hepatitis C prevalence among people who inject drugs in Mauritius. Respondent‐driven sampling (RDS), which is a widely adopted link tracing sampling design used to collect samples from hard‐to‐reach human populations, was used to collect this sample. The random‐walk approximation underlying many common RDS estimators assumes that each social relationship (edge) in the underlying social network has an equal probability of being traced in the collection of the sample. This assumption does not hold in practice. We show that certain RDS estimators are sensitive to the violation of this assumption. To address this limitation in current methodology, and the effect that it may have on prevalence estimates, we present a new method for improving RDS prevalence estimators using estimated edge inclusion probabilities, and we apply this to data from Mauritius.
Wave equations associated to Liouville systems and constant mean curvature equations

Abstract We study the wave analog of the Liouville equation for constant Gauss curvature on S 2 . We show the global existence in time for the sub-critical equation with data in H 1 ( S 2 ) × L 2 ( S 2 ) . For the super-critical and the critical equation we show the global existence in time assuming the data is symmetric with respect to antipodal points on S 2 . Use is made of the Moser–Trudinger inequalities for proving this result. For general data without symmetry assumptions and for the critical equation, we establish that if there is a blow-up, then it will occur at a single point of S 2 . We also consider the wave analog of the constant mean curvature equation in the Plateau formalism. This is a system. We establish finite time blow up if the energy of the initial data is larger than 8 π and with an additional subsidiary assumption. The threshold energy of 8 π arises because it is the energy of the basic bubble discovered by Brezis and Coron.
Attack-aware multi-sensor integration algorithm for autonomous vehicle navigation systems

In this paper, we propose a fault detection and isolation based attack-aware multi-sensor integration algorithm for the detection of cyberattacks in autonomous vehicle navigation systems. The proposed algorithm uses an extended Kalman filter to construct robust residuals in the presence of noise, and then uses a parametric statistical tool to identify cyberattacks. The parametric statistical tool is based on the residuals constructed by the measurement history rather than one measurement at a time in the properties of discrete-time signals and dynamic systems. This approach allows the proposed multi-sensor integration algorithm to provide quick detection and low false alarm rates for applications in dynamic systems. An example of INS/GNSS integration of autonomous navigation systems is presented to validate the proposed algorithm by using a software-in-the-loop simulation.
Accelerating polynomial homotopy continuation on a graphics processing unit with double double and quad double arithmetic

Numerical continuation methods track a solution path defined by a homotopy. The systems we consider are defined by polynomials in several variables with complex coefficients. For larger dimensions and degrees, the numerical conditioning worsens and hardware double precision becomes often insufficient to reach the end of the solution path. With double double and quad double arithmetic, we can solve larger problems that we could not solve with hardware double arithmetic, but at a higher computational cost. This cost overhead can be compensated by acceleration on a Graphics Processing Unit (GPU). We describe our implementation and report on computational results on benchmark polynomial systems.
On the Prony series representation of stretched exponential relaxation

Abstract Stretched exponential relaxation is a ubiquitous feature of homogeneous glasses. The stretched exponential decay function can be derived from the diffusion-trap model, which predicts certain critical values of the fractional stretching exponent, β . In practical implementations of glass relaxation models, it is computationally convenient to represent the stretched exponential function as a Prony series of simple exponentials. Here, we perform a comprehensive mathematical analysis of the Prony series approximation of the stretched exponential relaxation, including optimized coefficients for certain critical values of β . The fitting quality of the Prony series is analyzed as a function of the number of terms in the series. With a sufficient number of terms, the Prony series can accurately capture the time evolution of the stretched exponential function, including its “fat tail” at long times. However, it is unable to capture the divergence of the first-derivative of the stretched exponential function in the limit of zero time. We also present a frequency-domain analysis of the Prony series representation of the stretched exponential function and discuss its physical implications for the modeling of glass relaxation behavior.
The boundary of an affine invariant submanifold

We study the boundary of an affine invariant submanifold of a stratum of translation surfaces in a partial compactification consisting of all finite area Abelian differentials over nodal Riemann surfaces, modulo zero area components. The main result is a formula for the tangent space to the boundary. We also prove finiteness results concerning cylinders, a partial converse to the Cylinder Deformation Theorem, and a result generalizing part of the Veech dichotomy.
DxNAT — Deep neural networks for explaining non-recurring traffic congestion

Non-recurring traffic congestion is caused by temporary disruptions, such as accidents, sports games, adverse weather, etc. We use data related to real-time traffic speed, jam factors (a traffic congestion indicator), and events collected over a year from Nashville, TN to train a multi-layered deep neural network. The traffic dataset contains over 900 million data records. The network is thereafter used to classify the real-time data and identify anomalous operations. Compared with traditional approaches of using statistical or machine learning techniques, our model reaches an accuracy of 98.73 percent when identifying traffic congestion caused by football games. Our approach first encodes the traffic across a region as a scaled image. After that the image data from different timestamps is fused with event- and time-related data. Then a crossover operator is used as a data augmentation method to generate training datasets with more balanced classes. Finally, we use the receiver operating characteristic (ROC) analysis to tune the sensitivity of the classifier. We present the analysis of the training time and the inference time separately.
Catching Fire via "Likes": Inferring Topic Preferences of Trump Followers on Twitter

In this paper, we propose a framework to infer the topic preferences of Donald Trump's followers on Twitter. We first use latent Dirichlet allocation (LDA) to derive the weighted mixture of topics for each Trump tweet. Then we use negative binomial regression to model the "likes," with the weights of each topic serving as explanatory variables. Our study shows that attacking Democrats such as President Obama and former Secretary of State Hillary Clinton earns Trump the most "likes." Our framework of inference is generalizable to the study of other politicians.
Grothendieck rings of universal quantum groups

Abstract We determine the Grothendieck ring of finite-dimensional comodules for the free Hopf algebra on a matrix coalgebra, and similarly for the free Hopf algebra with bijective antipode and other related universal quantum groups. The results turn out to parallel those for Wang and Van Daeleʼs universal compact quantum groups and Bichonʼs generalization of those results to universal cosovereign Hopf algebras: in all cases the rings are isomorphic to those of non-commutative polynomials over certain sets, these sets varying from case to case. In most cases we are able to give more precise information about the multiplication table of the Grothendieck ring.
The resonant nonlinear scattering theory with bound states in the radiation continuum and the second harmonic generation

A nonlinear electromagnetic scattering problem is studied in the presence of bound states in the radiation continuum (or resonances with the vanishing width). It is shown that the solution is not analytic in the nonlinear susceptibility and the conventional perturbation theory fails due to strong evanescent fields that necessarily occur if the scattering system has resonances with the vanishing width. A non-perturbative approach is developed. It is then applied to the system of two parallel periodic subwavelength arrays of dielectric cylinders with a second order nonlinear susceptibility. This scattering system is known to have bound states in the radiation continuum. In particular, it is demonstrated that, for a wide range of values of the nonlinear susceptibility, the structure converts over 40% of the incident fundamental harmonic flux into the outgoing second harmonic flux when the distance between the arrays is as low as a half of the incident radiation wavelength. The effect is non-perturbative and solely attributed to the presence of bound states in the radiation continuum. The example demonstrates that bounds states in the radiation continuum can be used to substantially enhance and control optically nonlinear effects in nanophotonic devices.
Modeling Inter-country Connection from Geotagged News Reports: A Time-Series Analysis

The rapid development of big data techniques provides growing opportunities to investigate large-scale events that emerge over space and time. This research utilizes a unique open-access dataset, “The Global Data on Events, Location and Tone” (GDELT), to model how China has connected to the rest of the world, as well as predicting how this connection may evolve over time based on an autoregressive integrated moving average (ARIMA) model. Methodologically, we examined the effectiveness of traditional time series models in predicting trends in long-term mass media data. Empirically, we identified various types of ARIMA models to depict the connection patterns between China and its top 15 related countries. This study demonstrates the power of applying GDELT and big data analytics to investigate informative patterns for interdisciplinary researchers, as well as provides valuable references to interpret regional patterns and international relations in the age of instant access.
Distinct word length frequencies: distributions and symbol entropies

The distribution of frequency counts of distinct words by length in a language's vocabulary will be analyzed using two methods. The first, will look at the empirical distributions of several languages and derive a distribution that reasonably explains the number of distinct words as a function of length. We will be able to derive the frequency count, mean word length, and variance of word length based on the marginal probability of letters and spaces. The second, based on information theory, will demonstrate that the conditional entropies can also be used to estimate the frequency of distinct words of a given length in a language. In addition, it will be shown how these techniques can also be applied to estimate higher order entropies using vocabulary word length.
Filling the gaps smoothly

Abstract The calibration of a local volatility models to a given set of option prices is a classical problem of mathematical finance. It was considered in multiple papers where various solutions were proposed. In this paper an extension of the approach proposed in Lipton, Sepp 2011 is developed by (i) replacing a piecewise constant local variance construction with a piecewise linear one, and (ii) allowing non-zero interest rates and dividend yields. Our approach remains analytically tractable; it combines the Laplace transform in time with an analytical solution of the resulting spatial equations in terms of Kummer's degenerate hypergeometric functions.
ProxQuant: Quantized Neural Networks via Proximal Operators

To make deep neural networks feasible in resource-constrained environments (such as mobile devices), it is beneficial to quantize models by using low-precision weights. One common technique for quantizing neural networks is the straight-through gradient method, which enables back-propagation through the quantization mapping. Despite its empirical success, little is understood about why the straight-through gradient method works. Building upon a novel observation that the straight-through gradient method is in fact identical to the well-known Nesterov's dual-averaging algorithm on a quantization constrained optimization problem, we propose a more principled alternative approach, called ProxQuant, that formulates quantized network training as a regularized learning problem instead and optimizes it via the prox-gradient method. ProxQuant does back-propagation on the underlying full-precision vector and applies an efficient prox-operator in between stochastic gradient steps to encourage quantizedness. For quantizing ResNets and LSTMs, ProxQuant outperforms state-of-the-art results on binary quantization and is on par with state-of-the-art on multi-bit quantization. For binary quantization, our analysis shows both theoretically and experimentally that ProxQuant is more stable than the straight-through gradient method (i.e. BinaryConnect), challenging the indispensability of the straight-through gradient method and providing a powerful alternative.
Zariski decompositions of numerical cycle classes

We construct a Zariski decomposition for cycle classes of arbitrary codimension. This decomposition is an analogue of well-known constructions for divisors. Examples illustrate how Zariski decompositions of cycle classes reflect the geometry of the underlying space. We also analyze the birational behavior of Zariski decompositions, leading to a Fujita approximation-type result for curve classes.
Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin.

The past decade has seen a revolution in genomic technologies that enabled a flood of genome-wide profiling of chromatin marks. Recent literature tried to understand gene regulation by predicting gene expression from large-scale chromatin measurements. Two fundamental challenges exist for such learning tasks: (1) genome-wide chromatin signals are spatially structured, high-dimensional and highly modular; and (2) the core aim is to understand what are the relevant factors and how they work together. Previous studies either failed to model complex dependencies among input signals or relied on separate feature analysis to explain the decisions. This paper presents an attention-based deep learning approach; AttentiveChrome, that uses a unified architecture to model and to interpret dependencies among chromatin factors for controlling gene regulation. AttentiveChrome uses a hierarchy of multiple Long Short-Term Memory (LSTM) modules to encode the input signals and to model how various chromatin marks cooperate automatically. AttentiveChrome trains two levels of attention jointly with the target prediction, enabling it to attend differentially to relevant marks and to locate important positions per mark. We evaluate the model across 56 different cell types (tasks) in human. Not only is the proposed architecture more accurate, but its attention scores also provide a better interpretation than state-of-the-art feature visualization methods such as saliency map.
Recurrent Environment Simulators

Models that can simulate how environments change in response to actions can be used by agents to plan and act efficiently. We improve on previous environment simulators from high-dimensional pixel observations by introducing recurrent neural networks that are able to make temporally and spatially coherent predictions for hundreds of time-steps into the future. We present an in-depth analysis of the factors affecting performance, providing the most extensive attempt to advance the understanding of the properties of these models. We address the issue of computationally inefficiency with a model that does not need to generate a high-dimensional image at each time-step. We show that our approach can be used to improve exploration and is adaptable to many diverse environments, namely 10 Atari games, a 3D car racing environment, and complex 3D mazes.
Regolith behavior under asteroid-level gravity conditions: low-velocity impact experiments

The dusty regolith covering the surfaces of asteroids and planetary satellites differs in size, shape, and composition from terrestrial soil particles and is subject to environmental conditions very different from those found on Earth. This regolith evolves in a low ambient pressure and low-gravity environment. Its response to low-velocity impacts, such as those that may accompany human and robotic exploration activities, may be completely different than what is encountered on Earth. Experimental studies of the response of planetary regolith in the relevant environmental conditions are thus necessary to facilitate future Solar System exploration activities.We combined the results and provided new data analysis elements for a series of impact experiments into simulated planetary regolith in low-gravity conditions using two experimental setups and a range of microgravity platforms. The Physics of Regolith Impacts in Microgravity Experiment (PRIME) flew on several parabolic aircraft flights, enabling the recording of impacts into granular materials at speeds of ∼ 4–230 cm/s. The COLLisions Into Dust Experiment (COLLIDE) is conceptually close to the PRIME setup. It flew on the Space Shuttle in 1998 and 2001 and more recently on the Blue Origin New Shepard rocket, recording impacts into simulated regolith at speeds between 1 and 120 cm/s.Results of these experimental campaigns found that there is a significant change in the regolith behavior with the gravity environment. In a 10 −2g environment (with g being the gravity acceleration at the surface of the Earth), only embedding of the impactor was observed and ejecta production was produced for most impacts at > 20 cm/s. Once at microgravity levels ( 10 cm/s. The measured ejecta speeds were somewhat lower than the ones measured at reduced-gravity levels, but the ejected masses were higher. In general, the mean ejecta velocity shows a power-law dependence on the impact energy with an index of ∼ 0.5. When projectile rebound occurred, we observed that its coefficients of restitution on the bed of regolith simulant decrease by a factor of 10 with increasing impact speeds from ∼ 5 up to 100 cm/s. We could also observe an increased cohesion between the JSC-1 grains compared to the quartz sand targets.
Non-Orthogonal Multiple Access Based on Hybrid Beamforming for mmWave Systems

This paper aims to study the utilization of non-orthogonal multiple access (NOMA) in hybrid beamforming (HB) multiuser systems, called HB-NOMA, to serve a large number of mobile users. First, a sum-rate expression for the HB-NOMA problem is formulated. Second, an effective algorithm is proposed to maximize the sum-rate. Then, a lower bound is derived under two cases: i) the angle between the effective channel vectors of the MU with the highest channel gain and other MUs located inside a cluster is zero (or close to it) which is denoted by perfect correlation, and ii) this angle is non-zero, which we denote by imperfect correlation. For the second case, the relationship between the effective channels of two HB-NOMA users is modeled. The lower bound for the first case indicates that only the presence of inter-cluster interference and the use of an analog precoder negatively impact the sum-rate. However, in the case of imperfect correlation, which is more realistic, an inefficient MU cluster, can cause severe intra-cluster interference in the network. To verify our findings, numerical simulations have been conducted.
Preserving photon qubits in an unknown quantum state with Knill dynamical decoupling: Towards an all optical quantum memory

The implementation of polarization-based quantum communication is limited by signal loss and decoherence caused by the birefringence of a single-mode fiber. We investigate the Knill dynamical decoupling scheme, implemented using half-wave plates, to minimize decoherence and show that a fidelity greater than $99%$ can be achieved in the absence of rotation error and a fidelity greater than $96%$ can be achieved in the presence of rotation error. Such a scheme can be used to preserve any quantum state with high fidelity and has potential application for constructing all optical quantum delay lines, quantum memory, and quantum repeaters.
Constructing minimal periods of quadratic irrationalities in Zagier's reduction theory

Abstract Dirichlet's version of Gauss's reduction theory for indefinite binary quadratic forms includes a map from Gauss-reduced forms to strings of natural numbers. It attaches to a form the minimal period of the continued fraction of a quadratic irrationality associated with the form. When Zagier developed his own reduction theory, parallel to Dirichlet's, he omitted an analogue of this map. We define a new map on Zagier-reduced forms that serves as this analogue. We also define a map from the set of Gauss-reduced forms into the set of Zagier-reduced forms that gives a near-embedding of the structure of Gauss's reduction theory into that of Zagier's. From this perspective, Zagier-reduction becomes a refinement of Gauss-reduction.
Data Noising as Smoothing in Neural Network Language Models

Data noising is an effective technique for regularizing neural network models. While noising is widely adopted in application domains such as vision and speech, commonly used noising primitives have not been developed for discrete sequence-level settings such as language modeling. In this paper, we derive a connection between input noising in neural network language models and smoothing in $n$-gram models. Using this connection, we draw upon ideas from smoothing to develop effective noising schemes. We demonstrate performance gains when applying the proposed schemes to language modeling and machine translation. Finally, we provide empirical analysis validating the relationship between noising and smoothing.
Stability for implicit–explicit schemes for non-equilibrium kinetic systems in weighted spaces with symmetrization

Abstract We consider kinetic systems, and prove their stability working in weighted spaces in which the systems are symmetric. We prove stability for various explicit and implicit semi-discrete and fully discrete schemes. The applications include advective and diffusive transport coupled to the accumulation of immobile components governed by non-equilibrium relationships. We also discuss extensions to nonlinear relationships and multiple species.
BPS/CFT correspondence II: Instantons at crossroads, moduli and compactness theorem

Gieseker-Nakajima moduli spaces $M_{k}(n)$ parametrize the charge $k$ noncommutative $U(n)$ instantons on ${\bf R}^{4}$ and framed rank $n$ torsion free sheaves $\mathcal{E}$ on ${\bf C\bf P}^{2}$ with ${\rm ch}_{2}({\mathcal{E}}) = k$. They also serve as local models of the moduli spaces of instantons on general four-manifolds. We study the generalization of gauge theory in which the four dimensional spacetime is a stratified space $X$ immersed into a Calabi-Yau fourfold $Z$. The local model ${\bf M}_{k}({\vec n})$ of the corresponding instanton moduli space is the moduli space of charge $k$ (noncommutative) instantons on origami spacetimes. There, $X$ is modelled on a union of (up to six) coordinate complex planes ${\bf C}^{2}$ intersecting in $Z$ modelled on ${\bf C}^{4}$. The instantons are shared by the collection of four dimensional gauge theories sewn along two dimensional defect surfaces and defect points. We also define several quiver versions ${\bf M}_{\bf k}^{\gamma}({\vec{\bf n}})$ of ${\bf M}_{k}({\vec n})$, motivated by the considerations of sewn gauge theories on orbifolds ${\bf C}^{4}/{\Gamma}$. The geometry of the spaces ${\bf M}_{\bf k}^{\gamma}({\vec{\bf n}})$, more specifically the compactness of the set of torus-fixed points, for various tori, underlies the non-perturbative Dyson-Schwinger identities recently found to be satisfied by the correlation functions of $qq$-characters viewed as local gauge invariant operators in the ${\mathcal{N}}=2$ quiver gauge theories. The cohomological and K-theoretic operations defined using ${\bf M}_{k}({\vec n})$ and their quiver versions as correspondences provide the geometric counterpart of the $qq$-characters, line and surface defects.
A Light-powered, "Always-On", Smart Camera with Compressed Domain Gesture Detection

In this paper we propose an energy-efficient camera-based gesture recognition system powered by light energy for "always on" applications. Low energy consumption is achieved by directly extracting gesture features from the compressed measurements, which are the block averages and the linear combinations of the image sensor's pixel values. The gestures are recognized using a nearest-neighbour (NN) classifier followed by Dynamic Time Warping (DTW). The system has been implemented on an Analog Devices Black Fin ULP vision processor and powered by PV cells whose output is regulated by TI's DC-DC buck converter with Maximum Power Point Tracking (MPPT). Measured data reveals that with only 400 compressed measurements (768x compression ratio) per frame, the system is able to recognize key wake-up gestures with greater than 80% accuracy and only 95mJ of energy per frame. Owing to its fully self-powered operation, the proposed system can find wide applications in "always-on" vision systems such as in surveillance, robotics and consumer electronics with touch-less operation.
Stochastic agent-based simulations of social networks

The rapidly growing field of network analytics requires data sets for use in evaluation. Real world data often lack truth and simulated data lack narrative fidelity or statistical generality. This paper presents a novel, mixed-membership, agent-based simulation model to generate activity data with narrative power while providing statistical diversity through random draws. The model generalizes to a variety of network activity types such as Internet and cellular communications, human mobility, and social network interactions. The simulated actions over all agents can then drive an application specific observational model to render measurements as one would collect in real-world experiments. We apply this framework to human mobility and demonstrate its utility in generating high fidelity traffic data for network analytics.
Shock formation in solutions to the 2D compressible Euler equations in the presence of non-zero vorticity

We study the Cauchy problem for the compressible Euler equations in two spatial dimensions under any physical barotropic equation of state except that of a Chaplygin gas. We prove that the well-known phenomenon of shock formation in simple plane wave solutions, starting from smooth initial data, is stable under perturbations of the initial data that break the plane symmetry. Moreover, we provide a sharp asymptotic description of the singularity formation. The new feature of our work is that the perturbed solutions are allowed to have small but non-zero vorticity, even at the location of the shock. Thus, our results provide the first constructive description of the vorticity near a singularity formed from compression. Specifically, the vorticity remains uniformly bounded, while the vorticity divided by the density exhibits even more regular behavior: the ratio remains uniformly Lipschitz relative to the standard Cartesian coordinates. To control the vorticity, we rely on a coalition of new geometric and analytic insights that complement the ones used by Christodoulou in his groundbreaking, sharp proof of shock formation in vorticity-free regions. In particular, we rely on a new formulation of the compressible Euler equations (derived in a companion article) exhibiting remarkable structures. To derive estimates, we construct an eikonal function adapted to the acoustic characteristics (which correspond to sound wave propagation) and a related set of geometric coordinates and differential operators. Thanks to the remarkable structure of the equations, the same set of coordinates and differential operators can be used to analyze the vorticity, whose characteristics are transversal to the acoustic characteristics. In particular, our work provides the first constructive description of shock formation without symmetry assumptions in a system with multiple speeds.
Some results about zero‐cycles on abelian and semi‐abelian varieties

In this short note we extend some results obtained in \cite{Gazaki2015}. First, we prove that for an abelian variety $A$ with good ordinary reduction over a finite extension of $\mathbb{Q}_p$ with $p$ an odd prime, the Albanese kernel of $A$ is the direct sum of its maximal divisible subgroup and a torsion group. Second, for a semi-abelian variety $G$ over a perfect field $k$, we construct a decreasing integral filtration $\{F^r\}_{r\geq 0}$ of Suslin's singular homology group, $H_0^{sing}(G)$, such that the successive quotients are isomorphic to a certain Somekawa K-group.